manual-references-2020-01-29.json

[
  {
    "id": "3qm8sXnB",
    "URL": "https://arxiv.org/abs/1106.5730",
    "number": "1106.5730",
    "title": "HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent",
    "issued": {
      "date-parts": [
        [
          2011,
          11,
          14
        ]
      ]
    },
    "author": [
      {
        "given": "Feng",
        "family": "Niu"
      },
      {
        "given": "Benjamin",
        "family": "Recht"
      },
      {
        "given": "Christopher",
        "family": "Re"
      },
      {
        "given": "Stephen J.",
        "family": "Wright"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks. Several researchers have recently proposed schemes to parallelize SGD, but all require performance-destroying memory locking and synchronization. This work aims to show using novel theoretical analysis, algorithms, and implementation that SGD can be implemented without any locking. We present an update scheme called HOGWILD! which allows processors access to shared memory with the possibility of overwriting each other's work. We show that when the associated optimization problem is sparse, meaning most gradient updates only modify small parts of the decision variable, then HOGWILD! achieves a nearly optimal rate of convergence. We demonstrate experimentally that HOGWILD! outperforms alternative schemes that use locking by an order of magnitude. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1106.5730"
  },
  {
    "id": "8RAYEOPl",
    "URL": "https://arxiv.org/abs/1206.7051",
    "number": "1206.7051",
    "title": "Stochastic Variational Inference",
    "issued": {
      "date-parts": [
        [
          2013,
          4,
          24
        ]
      ]
    },
    "author": [
      {
        "given": "Matt",
        "family": "Hoffman"
      },
      {
        "given": "David M.",
        "family": "Blei"
      },
      {
        "given": "Chong",
        "family": "Wang"
      },
      {
        "given": "John",
        "family": "Paisley"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1206.7051"
  },
  {
    "id": "g2vvbB91",
    "URL": "https://arxiv.org/abs/1212.0901v2",
    "number": "1212.0901v2",
    "version": "v2",
    "title": "Advances in Optimizing Recurrent Networks",
    "issued": {
      "date-parts": [
        [
          2012,
          12,
          4
        ]
      ]
    },
    "author": [
      {
        "literal": "Yoshua Bengio"
      },
      {
        "literal": "Nicolas Boulanger-Lewandowski"
      },
      {
        "literal": "Razvan Pascanu"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "After a more than decade-long period of relatively little research activity in the area of recurrent neural networks, several new developments will be reviewed here that have allowed substantial progress both in understanding and in technical solutions towards more efficient training of recurrent networks. These advances have been motivated by and related to the optimization issues surrounding deep learning. Although recurrent networks are extremely powerful in what they can in principle represent in terms of modelling sequences,their training is plagued by two aspects of the same issue regarding the learning of long-term dependencies. Experiments reported here evaluate the use of clipping gradients, spanning longer time ranges with leaky integration, advanced momentum techniques, using more powerful output probability models, and encouraging sparser gradients to help symmetry breaking and credit assignment. The experiments are performed on text and music data and show off the combined effects of these techniques in generally improving both training and test error.",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1212.0901v2"
  },
  {
    "id": "1GhHIDxuW",
    "URL": "https://arxiv.org/abs/1301.3781",
    "number": "1301.3781",
    "title": "Efficient Estimation of Word Representations in Vector Space",
    "issued": {
      "date-parts": [
        [
          2013,
          9,
          10
        ]
      ]
    },
    "author": [
      {
        "given": "Tomas",
        "family": "Mikolov"
      },
      {
        "given": "Kai",
        "family": "Chen"
      },
      {
        "given": "Greg",
        "family": "Corrado"
      },
      {
        "given": "Jeffrey",
        "family": "Dean"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1301.3781"
  },
  {
    "id": "15y7iq6HF",
    "URL": "https://arxiv.org/abs/1308.0850",
    "number": "1308.0850",
    "title": "Generating Sequences With Recurrent Neural Networks",
    "issued": {
      "date-parts": [
        [
          2014,
          6,
          6
        ]
      ]
    },
    "author": [
      {
        "given": "Alex",
        "family": "Graves"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1308.0850"
  },
  {
    "id": "voh0OiT2",
    "URL": "https://arxiv.org/abs/1311.2901",
    "number": "1311.2901",
    "title": "Visualizing and Understanding Convolutional Networks",
    "issued": {
      "date-parts": [
        [
          2013,
          12,
          2
        ]
      ]
    },
    "author": [
      {
        "given": "Matthew D",
        "family": "Zeiler"
      },
      {
        "given": "Rob",
        "family": "Fergus"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. We also perform an ablation study to discover the performance contribution from different model layers. This enables us to find model architectures that outperform Krizhevsky \\etal on the ImageNet classification benchmark. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1311.2901"
  },
  {
    "id": "1YcKYTvO",
    "URL": "https://arxiv.org/abs/1312.6034",
    "number": "1312.6034",
    "title": "Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps",
    "issued": {
      "date-parts": [
        [
          2014,
          4,
          22
        ]
      ]
    },
    "author": [
      {
        "given": "Karen",
        "family": "Simonyan"
      },
      {
        "given": "Andrea",
        "family": "Vedaldi"
      },
      {
        "given": "Andrew",
        "family": "Zisserman"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks (ConvNets). We consider two visualisation techniques, based on computing the gradient of the class score with respect to the input image. The first one generates an image, which maximises the class score [Erhan et al., 2009], thus visualising the notion of the class, captured by a ConvNet. The second technique computes a class saliency map, specific to a given image and class. We show that such maps can be employed for weakly supervised object segmentation using classification ConvNets. Finally, we establish the connection between the gradient-based ConvNet visualisation methods and deconvolutional networks [Zeiler et al., 2013]. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1312.6034"
  },
  {
    "id": "1AhGoHZP9",
    "URL": "https://arxiv.org/abs/1312.6184",
    "number": "1312.6184",
    "title": "Do Deep Nets Really Need to be Deep?",
    "issued": {
      "date-parts": [
        [
          2014,
          10,
          14
        ]
      ]
    },
    "author": [
      {
        "given": "Lei Jimmy",
        "family": "Ba"
      },
      {
        "given": "Rich",
        "family": "Caruana"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Currently, deep neural networks are the state of the art on problems such as speech recognition and computer vision. In this extended abstract, we show that shallow feed-forward networks can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models. Moreover, in some cases the shallow neural nets can learn these deep functions using a total number of parameters similar to the original deep model. We evaluate our method on the TIMIT phoneme recognition task and are able to train shallow fully-connected nets that perform similarly to complex, well-engineered, deep convolutional architectures. Our success in training shallow neural nets to mimic deeper models suggests that there probably exist better algorithms for training shallow feed-forward nets than those currently available. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1312.6184"
  },
  {
    "id": "1Fel6Bdb8",
    "URL": "https://arxiv.org/abs/1312.6199",
    "number": "1312.6199",
    "title": "Intriguing properties of neural networks",
    "issued": {
      "date-parts": [
        [
          2014,
          2,
          20
        ]
      ]
    },
    "author": [
      {
        "given": "Christian",
        "family": "Szegedy"
      },
      {
        "given": "Wojciech",
        "family": "Zaremba"
      },
      {
        "given": "Ilya",
        "family": "Sutskever"
      },
      {
        "given": "Joan",
        "family": "Bruna"
      },
      {
        "given": "Dumitru",
        "family": "Erhan"
      },
      {
        "given": "Ian",
        "family": "Goodfellow"
      },
      {
        "given": "Rob",
        "family": "Fergus"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties.\n  First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks.\n  Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. We can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network's prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input. ",
    "note": "license: http://creativecommons.org/licenses/by/3.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1312.6199"
  },
  {
    "id": "8t43CQ9m",
    "URL": "https://arxiv.org/abs/1403.1347",
    "number": "1403.1347",
    "title": "Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction",
    "issued": {
      "date-parts": [
        [
          2014,
          3,
          7
        ]
      ]
    },
    "author": [
      {
        "given": "Jian",
        "family": "Zhou"
      },
      {
        "given": "Olga G.",
        "family": "Troyanskaya"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Predicting protein secondary structure is a fundamental problem in protein structure prediction. Here we present a new supervised generative stochastic network (GSN) based method to predict local secondary structure with deep hierarchical representations. GSN is a recently proposed deep learning technique (Bengio & Thibodeau-Laufer, 2013) to globally train deep generative model. We present the supervised extension of GSN, which learns a Markov chain to sample from a conditional distribution, and applied it to protein structure prediction. To scale the model to full-sized, high-dimensional data, like protein sequences with hundreds of amino acids, we introduce a convolutional architecture, which allows efficient learning across multiple layers of hierarchical representations. Our architecture uniquely focuses on predicting structured low-level labels informed with both low and high-level representations learned by the model. In our application this corresponds to labeling the secondary structure state of each amino-acid residue. We trained and tested the model on separate sets of non-homologous proteins sharing less than 30% sequence identity. Our model achieves 66.4% Q8 accuracy on the CB513 dataset, better than the previously reported best performance 64.9% (Wang et al., 2011) for this challenging secondary structure prediction problem. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1403.1347"
  },
  {
    "id": "ZSVsnPVO",
    "URL": "https://arxiv.org/abs/1404.5997",
    "number": "1404.5997",
    "title": "One weird trick for parallelizing convolutional neural networks",
    "issued": {
      "date-parts": [
        [
          2014,
          4,
          29
        ]
      ]
    },
    "author": [
      {
        "given": "Alex",
        "family": "Krizhevsky"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional neural networks. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1404.5997"
  },
  {
    "id": "1Dzz0P0qr",
    "URL": "https://arxiv.org/abs/1406.1231",
    "number": "1406.1231",
    "title": "Multi-task Neural Networks for QSAR Predictions",
    "issued": {
      "date-parts": [
        [
          2014,
          6,
          6
        ]
      ]
    },
    "author": [
      {
        "given": "George E.",
        "family": "Dahl"
      },
      {
        "given": "Navdeep",
        "family": "Jaitly"
      },
      {
        "given": "Ruslan",
        "family": "Salakhutdinov"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Although artificial neural networks have occasionally been used for Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) studies in the past, the literature has of late been dominated by other machine learning techniques such as random forests. However, a variety of new neural net techniques along with successful applications in other domains have renewed interest in network approaches. In this work, inspired by the winning team's use of neural networks in a recent QSAR competition, we used an artificial neural network to learn a function that predicts activities of compounds for multiple assays at the same time. We conducted experiments leveraging recent methods for dealing with overfitting in neural networks as well as other tricks from the neural networks literature. We compared our methods to alternative methods reported to perform well on these tasks and found that our neural net methods provided superior performance. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1406.1231"
  },
  {
    "id": "haHzVaaz",
    "URL": "https://arxiv.org/abs/1409.0473",
    "number": "1409.0473",
    "title": "Neural Machine Translation by Jointly Learning to Align and Translate",
    "issued": {
      "date-parts": [
        [
          2016,
          5,
          23
        ]
      ]
    },
    "author": [
      {
        "given": "Dzmitry",
        "family": "Bahdanau"
      },
      {
        "given": "Kyunghyun",
        "family": "Cho"
      },
      {
        "given": "Yoshua",
        "family": "Bengio"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1409.0473"
  },
  {
    "id": "2cMhMv5A",
    "URL": "https://arxiv.org/abs/1409.3215",
    "number": "1409.3215",
    "title": "Sequence to Sequence Learning with Neural Networks",
    "issued": {
      "date-parts": [
        [
          2014,
          12,
          16
        ]
      ]
    },
    "author": [
      {
        "given": "Ilya",
        "family": "Sutskever"
      },
      {
        "given": "Oriol",
        "family": "Vinyals"
      },
      {
        "given": "Quoc V.",
        "family": "Le"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT'14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous best result on this task. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1409.3215"
  },
  {
    "id": "YwdqeYZi",
    "URL": "https://arxiv.org/abs/1410.0759",
    "number": "1410.0759",
    "title": "cuDNN: Efficient Primitives for Deep Learning",
    "issued": {
      "date-parts": [
        [
          2014,
          12,
          19
        ]
      ]
    },
    "author": [
      {
        "given": "Sharan",
        "family": "Chetlur"
      },
      {
        "given": "Cliff",
        "family": "Woolley"
      },
      {
        "given": "Philippe",
        "family": "Vandermersch"
      },
      {
        "given": "Jonathan",
        "family": "Cohen"
      },
      {
        "given": "John",
        "family": "Tran"
      },
      {
        "given": "Bryan",
        "family": "Catanzaro"
      },
      {
        "given": "Evan",
        "family": "Shelhamer"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We present a library of efficient implementations of deep learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult and time-consuming. As parallel architectures evolve, kernels must be reoptimized, which makes maintaining codebases difficult over time. Similar issues have long been addressed in the HPC community by libraries such as the Basic Linear Algebra Subroutines (BLAS). However, there is no analogous library for deep learning. Without such a library, researchers implementing deep learning workloads on parallel processors must create and optimize their own implementations of the main computational kernels, and this work must be repeated as new parallel processors emerge. To address this problem, we have created a library similar in intent to BLAS, with optimized routines for deep learning workloads. Our implementation contains routines for GPUs, although similarly to the BLAS library, these routines could be implemented for other platforms. The library is easy to integrate into existing frameworks, and provides optimized performance and memory usage. For example, integrating cuDNN into Caffe, a popular framework for convolutional networks, improves performance by 36% on a standard model while also reducing memory consumption. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1410.0759"
  },
  {
    "id": "pxdeuhMS",
    "URL": "https://arxiv.org/abs/1411.2581v1",
    "number": "1411.2581v1",
    "version": "v1",
    "title": "Deep Exponential Families",
    "issued": {
      "date-parts": [
        [
          2014,
          11,
          10
        ]
      ]
    },
    "author": [
      {
        "literal": "Rajesh Ranganath"
      },
      {
        "literal": "Linpeng Tang"
      },
      {
        "literal": "Laurent Charlin"
      },
      {
        "literal": "David M. Blei"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "We describe \\textit{deep exponential families} (DEFs), a class of latent variable models that are inspired by the hidden structures used in deep neural networks. DEFs capture a hierarchy of dependencies between latent variables, and are easily generalized to many settings through exponential families. We perform inference using recent \"black box\" variational inference techniques. We then evaluate various DEFs on text and combine multiple DEFs into a model for pairwise recommendation data. In an extensive study, we show that going beyond one layer improves predictions for DEFs. We demonstrate that DEFs find interesting exploratory structure in large data sets, and give better predictive performance than state-of-the-art models.",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1411.2581v1"
  },
  {
    "id": "19mGl6pfy",
    "URL": "https://arxiv.org/abs/1412.0035",
    "number": "1412.0035",
    "title": "Understanding Deep Image Representations by Inverting Them",
    "issued": {
      "date-parts": [
        [
          2014,
          12,
          2
        ]
      ]
    },
    "author": [
      {
        "given": "Aravindh",
        "family": "Mahendran"
      },
      {
        "given": "Andrea",
        "family": "Vedaldi"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Image representations, from SIFT and Bag of Visual Words to Convolutional Neural Networks (CNNs), are a crucial component of almost any image understanding system. Nevertheless, our understanding of them remains limited. In this paper we conduct a direct analysis of the visual information contained in representations by asking the following question: given an encoding of an image, to which extent is it possible to reconstruct the image itself? To answer this question we contribute a general framework to invert representations. We show that this method can invert representations such as HOG and SIFT more accurately than recent alternatives while being applicable to CNNs too. We then use this technique to study the inverse of recent state-of-the-art CNN image representations for the first time. Among our findings, we show that several layers in CNNs retain photographically accurate information about the image, with different degrees of geometric and photometric invariance. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1412.0035"
  },
  {
    "id": "1AkF8Wsv7",
    "URL": "https://arxiv.org/abs/1412.1897v4",
    "number": "1412.1897v4",
    "version": "v4",
    "title": "Deep Neural Networks are Easily Fooled: High Confidence Predictions for\n  Unrecognizable Images",
    "issued": {
      "date-parts": [
        [
          2014,
          12,
          5
        ]
      ]
    },
    "author": [
      {
        "literal": "Anh Nguyen"
      },
      {
        "literal": "Jason Yosinski"
      },
      {
        "literal": "Jeff Clune"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences remain between computer and human vision. A recent study revealed that changing an image (e.g. of a lion) in a way imperceptible to humans can cause a DNN to label the image as something else entirely (e.g. mislabeling a lion a library). Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence (e.g. labeling with certainty that white noise static is a lion). Specifically, we take convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and then find images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class. It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects, which we call \"fooling images\" (more generally, fooling examples). Our results shed light on interesting differences between human vision and current DNNs, and raise questions about the generality of DNN computer vision.",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1412.1897v4"
  },
  {
    "id": "UtcyntjF",
    "URL": "https://arxiv.org/abs/1412.6572",
    "number": "1412.6572",
    "title": "Explaining and Harnessing Adversarial Examples",
    "issued": {
      "date-parts": [
        [
          2015,
          3,
          24
        ]
      ]
    },
    "author": [
      {
        "given": "Ian J.",
        "family": "Goodfellow"
      },
      {
        "given": "Jonathon",
        "family": "Shlens"
      },
      {
        "given": "Christian",
        "family": "Szegedy"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1412.6572"
  },
  {
    "id": "f2L6isRj",
    "URL": "https://arxiv.org/abs/1412.6806",
    "number": "1412.6806",
    "title": "Striving for Simplicity: The All Convolutional Net",
    "issued": {
      "date-parts": [
        [
          2015,
          4,
          14
        ]
      ]
    },
    "author": [
      {
        "given": "Jost Tobias",
        "family": "Springenberg"
      },
      {
        "given": "Alexey",
        "family": "Dosovitskiy"
      },
      {
        "given": "Thomas",
        "family": "Brox"
      },
      {
        "given": "Martin",
        "family": "Riedmiller"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small number of fully connected layers. We re-evaluate the state of the art for object recognition from small images with convolutional networks, questioning the necessity of different components in the pipeline. We find that max-pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks. Following this finding -- and building on other recent work for finding simple network structures -- we propose a new architecture that consists solely of convolutional layers and yields competitive or state of the art performance on several object recognition datasets (CIFAR-10, CIFAR-100, ImageNet). To analyze the network we introduce a new variant of the \"deconvolution approach\" for visualizing features learned by CNNs, which can be applied to a broader range of network structures than existing approaches. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1412.6806"
  },
  {
    "id": "1G3owNNps",
    "URL": "https://arxiv.org/abs/1412.7024",
    "number": "1412.7024",
    "title": "Training deep neural networks with low precision multiplications",
    "issued": {
      "date-parts": [
        [
          2015,
          9,
          24
        ]
      ]
    },
    "author": [
      {
        "given": "Matthieu",
        "family": "Courbariaux"
      },
      {
        "given": "Yoshua",
        "family": "Bengio"
      },
      {
        "given": "Jean-Pierre",
        "family": "David"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Multipliers are the most space and power-hungry arithmetic operators of the digital implementation of deep neural networks. We train a set of state-of-the-art neural networks (Maxout networks) on three benchmark datasets: MNIST, CIFAR-10 and SVHN. They are trained with three distinct formats: floating point, fixed point and dynamic fixed point. For each of those datasets and for each of those formats, we assess the impact of the precision of the multiplications on the final error after training. We find that very low precision is sufficient not just for running trained networks but also for training them. For example, it is possible to train Maxout networks with 10 bits multiplications. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1412.7024"
  },
  {
    "id": "yAoN5gTU",
    "URL": "https://arxiv.org/abs/1502.02072",
    "number": "1502.02072",
    "title": "Massively Multitask Networks for Drug Discovery",
    "issued": {
      "date-parts": [
        [
          2015,
          2,
          10
        ]
      ]
    },
    "author": [
      {
        "given": "Bharath",
        "family": "Ramsundar"
      },
      {
        "given": "Steven",
        "family": "Kearnes"
      },
      {
        "given": "Patrick",
        "family": "Riley"
      },
      {
        "given": "Dale",
        "family": "Webster"
      },
      {
        "given": "David",
        "family": "Konerding"
      },
      {
        "given": "Vijay",
        "family": "Pande"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1502.02072"
  },
  {
    "id": "CKcJuj03",
    "URL": "https://arxiv.org/abs/1502.02551",
    "number": "1502.02551",
    "title": "Deep Learning with Limited Numerical Precision",
    "issued": {
      "date-parts": [
        [
          2015,
          2,
          11
        ]
      ]
    },
    "author": [
      {
        "given": "Suyog",
        "family": "Gupta"
      },
      {
        "given": "Ankur",
        "family": "Agrawal"
      },
      {
        "given": "Kailash",
        "family": "Gopalakrishnan"
      },
      {
        "given": "Pritish",
        "family": "Narayanan"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a crucial role in determining the network's behavior during training. Our results show that deep networks can be trained using only 16-bit wide fixed-point number representation when using stochastic rounding, and incur little to no degradation in the classification accuracy. We also demonstrate an energy-efficient hardware accelerator that implements low-precision fixed-point arithmetic with stochastic rounding. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1502.02551"
  },
  {
    "id": "iWxvn0xF",
    "URL": "https://arxiv.org/abs/1502.02791",
    "number": "1502.02791",
    "title": "Learning Transferable Features with Deep Adaptation Networks",
    "issued": {
      "date-parts": [
        [
          2015,
          5,
          28
        ]
      ]
    },
    "author": [
      {
        "given": "Mingsheng",
        "family": "Long"
      },
      {
        "given": "Yue",
        "family": "Cao"
      },
      {
        "given": "Jianmin",
        "family": "Wang"
      },
      {
        "given": "Michael I.",
        "family": "Jordan"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation Network (DAN) architecture, which generalizes deep convolutional neural network to the domain adaptation scenario. In DAN, hidden representations of all task-specific layers are embedded in a reproducing kernel Hilbert space where the mean embeddings of different domain distributions can be explicitly matched. The domain discrepancy is further reduced using an optimal multi-kernel selection method for mean embedding matching. DAN can learn transferable features with statistical guarantees, and can scale linearly by unbiased estimate of kernel embedding. Extensive empirical evidence shows that the proposed architecture yields state-of-the-art image classification error rates on standard domain adaptation benchmarks. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1502.02791"
  },
  {
    "id": "yHn4SDRI",
    "URL": "https://arxiv.org/abs/1502.03044",
    "number": "1502.03044",
    "title": "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention",
    "issued": {
      "date-parts": [
        [
          2016,
          4,
          20
        ]
      ]
    },
    "author": [
      {
        "given": "Kelvin",
        "family": "Xu"
      },
      {
        "given": "Jimmy",
        "family": "Ba"
      },
      {
        "given": "Ryan",
        "family": "Kiros"
      },
      {
        "given": "Kyunghyun",
        "family": "Cho"
      },
      {
        "given": "Aaron",
        "family": "Courville"
      },
      {
        "given": "Ruslan",
        "family": "Salakhutdinov"
      },
      {
        "given": "Richard",
        "family": "Zemel"
      },
      {
        "given": "Yoshua",
        "family": "Bengio"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr8k, Flickr30k and MS COCO. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1502.03044"
  },
  {
    "id": "1CRF3gAV",
    "URL": "https://arxiv.org/abs/1503.02531",
    "number": "1503.02531",
    "title": "Distilling the Knowledge in a Neural Network",
    "issued": {
      "date-parts": [
        [
          2015,
          3,
          10
        ]
      ]
    },
    "author": [
      {
        "given": "Geoffrey",
        "family": "Hinton"
      },
      {
        "given": "Oriol",
        "family": "Vinyals"
      },
      {
        "given": "Jeff",
        "family": "Dean"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1503.02531"
  },
  {
    "id": "13KjSCKB2",
    "URL": "https://arxiv.org/abs/1504.04343",
    "number": "1504.04343",
    "title": "Caffe con Troll: Shallow Ideas to Speed Up Deep Learning",
    "issued": {
      "date-parts": [
        [
          2015,
          5,
          28
        ]
      ]
    },
    "author": [
      {
        "given": "Stefan",
        "family": "Hadjis"
      },
      {
        "given": "Firas",
        "family": "Abuzaid"
      },
      {
        "given": "Ce",
        "family": "Zhang"
      },
      {
        "given": "Christopher",
        "family": "Ré"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We present Caffe con Troll (CcT), a fully compatible end-to-end version of the popular framework Caffe with rebuilt internals. We built CcT to examine the performance characteristics of training and deploying general-purpose convolutional neural networks across different hardware architectures. We find that, by employing standard batching optimizations for CPU training, we achieve a 4.5x throughput improvement over Caffe on popular networks like CaffeNet. Moreover, with these improvements, the end-to-end training time for CNNs is directly proportional to the FLOPS delivered by the CPU, which enables us to efficiently train hybrid CPU-GPU systems for CNNs. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1504.04343"
  },
  {
    "id": "15lYGmZpY",
    "URL": "https://arxiv.org/abs/1504.04788",
    "number": "1504.04788",
    "title": "Compressing Neural Networks with the Hashing Trick",
    "issued": {
      "date-parts": [
        [
          2015,
          4,
          21
        ]
      ]
    },
    "author": [
      {
        "given": "Wenlin",
        "family": "Chen"
      },
      {
        "given": "James T.",
        "family": "Wilson"
      },
      {
        "given": "Stephen",
        "family": "Tyree"
      },
      {
        "given": "Kilian Q.",
        "family": "Weinberger"
      },
      {
        "given": "Yixin",
        "family": "Chen"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-increasing data set sizes; however mobile devices are designed with very little memory and cannot store such large models. We present a novel network architecture, HashedNets, that exploits inherent redundancy in neural networks to achieve drastic reductions in model sizes. HashedNets uses a low-cost hash function to randomly group connection weights into hash buckets, and all connections within the same hash bucket share a single parameter value. These parameters are tuned to adjust to the HashedNets weight sharing architecture with standard backprop during training. Our hashing procedure introduces no additional memory overhead, and we demonstrate on several benchmark data sets that HashedNets shrink the storage requirements of neural networks substantially while mostly preserving generalization performance. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1504.04788"
  },
  {
    "id": "rZnxDitd",
    "URL": "https://arxiv.org/abs/1505.06807",
    "number": "1505.06807",
    "title": "MLlib: Machine Learning in Apache Spark",
    "issued": {
      "date-parts": [
        [
          2015,
          5,
          27
        ]
      ]
    },
    "author": [
      {
        "given": "Xiangrui",
        "family": "Meng"
      },
      {
        "given": "Joseph",
        "family": "Bradley"
      },
      {
        "given": "Burak",
        "family": "Yavuz"
      },
      {
        "given": "Evan",
        "family": "Sparks"
      },
      {
        "given": "Shivaram",
        "family": "Venkataraman"
      },
      {
        "given": "Davies",
        "family": "Liu"
      },
      {
        "given": "Jeremy",
        "family": "Freeman"
      },
      {
        "given": "DB",
        "family": "Tsai"
      },
      {
        "given": "Manish",
        "family": "Amde"
      },
      {
        "given": "Sean",
        "family": "Owen"
      },
      {
        "given": "Doris",
        "family": "Xin"
      },
      {
        "given": "Reynold",
        "family": "Xin"
      },
      {
        "given": "Michael J.",
        "family": "Franklin"
      },
      {
        "given": "Reza",
        "family": "Zadeh"
      },
      {
        "given": "Matei",
        "family": "Zaharia"
      },
      {
        "given": "Ameet",
        "family": "Talwalkar"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark's open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives. Shipped with Spark, MLlib supports several languages and provides a high-level API that leverages Spark's rich ecosystem to simplify the development of end-to-end machine learning pipelines. MLlib has experienced a rapid growth due to its vibrant open-source community of over 140 contributors, and includes extensive documentation to support further growth and to let users quickly get up to speed. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1505.06807"
  },
  {
    "id": "nu0eLZr0",
    "URL": "https://arxiv.org/abs/1505.07818",
    "number": "1505.07818",
    "title": "Domain-Adversarial Training of Neural Networks",
    "issued": {
      "date-parts": [
        [
          2016,
          5,
          27
        ]
      ]
    },
    "author": [
      {
        "given": "Yaroslav",
        "family": "Ganin"
      },
      {
        "given": "Evgeniya",
        "family": "Ustinova"
      },
      {
        "given": "Hana",
        "family": "Ajakan"
      },
      {
        "given": "Pascal",
        "family": "Germain"
      },
      {
        "given": "Hugo",
        "family": "Larochelle"
      },
      {
        "given": "François",
        "family": "Laviolette"
      },
      {
        "given": "Mario",
        "family": "Marchand"
      },
      {
        "given": "Victor",
        "family": "Lempitsky"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main learning task on the source domain and (ii) indiscriminate with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation and stochastic gradient descent, and can thus be implemented with little effort using any of the deep learning packages. We demonstrate the success of our approach for two distinct classification problems (document sentiment analysis and image classification), where state-of-the-art domain adaptation performance on standard benchmarks is achieved. We also validate the approach for descriptor learning task in the context of person re-identification application. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1505.07818"
  },
  {
    "id": "2cpYveR4",
    "URL": "https://arxiv.org/abs/1506.02078",
    "number": "1506.02078",
    "title": "Visualizing and Understanding Recurrent Networks",
    "issued": {
      "date-parts": [
        [
          2015,
          11,
          18
        ]
      ]
    },
    "author": [
      {
        "given": "Andrej",
        "family": "Karpathy"
      },
      {
        "given": "Justin",
        "family": "Johnson"
      },
      {
        "given": "Li",
        "family": "Fei-Fei"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Recurrent Neural Networks (RNNs), and specifically a variant with Long Short-Term Memory (LSTM), are enjoying renewed interest as a result of successful applications in a wide range of machine learning problems that involve sequential data. However, while LSTMs provide exceptional results in practice, the source of their performance and their limitations remain rather poorly understood. Using character-level language models as an interpretable testbed, we aim to bridge this gap by providing an analysis of their representations, predictions and error types. In particular, our experiments reveal the existence of interpretable cells that keep track of long-range dependencies such as line lengths, quotes and brackets. Moreover, our comparative analysis with finite horizon n-gram models traces the source of the LSTM improvements to long-range structural dependencies. Finally, we provide analysis of the remaining errors and suggests areas for further study. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1506.02078"
  },
  {
    "id": "1FDihfnM",
    "URL": "https://arxiv.org/abs/1506.02142",
    "number": "1506.02142",
    "title": "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning",
    "issued": {
      "date-parts": [
        [
          2016,
          10,
          5
        ]
      ]
    },
    "author": [
      {
        "given": "Yarin",
        "family": "Gal"
      },
      {
        "given": "Zoubin",
        "family": "Ghahramani"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNs -- extracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We perform an extensive study of the properties of dropout's uncertainty. Various network architectures and non-linearities are assessed on tasks of regression and classification, using MNIST as an example. We show a considerable improvement in predictive log-likelihood and RMSE compared to existing state-of-the-art methods, and finish by using dropout's uncertainty in deep reinforcement learning. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1506.02142"
  },
  {
    "id": "17i18PMkR",
    "URL": "https://arxiv.org/abs/1506.06579",
    "number": "1506.06579",
    "title": "Understanding Neural Networks Through Deep Visualization",
    "issued": {
      "date-parts": [
        [
          2015,
          6,
          23
        ]
      ]
    },
    "author": [
      {
        "given": "Jason",
        "family": "Yosinski"
      },
      {
        "given": "Jeff",
        "family": "Clune"
      },
      {
        "given": "Anh",
        "family": "Nguyen"
      },
      {
        "given": "Thomas",
        "family": "Fuchs"
      },
      {
        "given": "Hod",
        "family": "Lipson"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recognize natural images. However, our understanding of how these models work, especially what computations they perform at intermediate layers, has lagged behind. Progress in the field will be further accelerated by the development of better tools for visualizing and interpreting neural nets. We introduce two such tools here. The first is a tool that visualizes the activations produced on each layer of a trained convnet as it processes an image or video (e.g. a live webcam stream). We have found that looking at live activations that change in response to user input helps build valuable intuitions about how convnets work. The second tool enables visualizing features at each layer of a DNN via regularized optimization in image space. Because previous versions of this idea produced less recognizable images, here we introduce several new regularization methods that combine to produce qualitatively clearer, more interpretable visualizations. Both tools are open source and work on a pre-trained convnet with minimal setup. ",
    "note": "license: http://creativecommons.org/licenses/by-nc-sa/3.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1506.06579"
  },
  {
    "id": "aClNvbyM",
    "URL": "https://arxiv.org/abs/1507.01239",
    "number": "1507.01239",
    "title": "Experiments on Parallel Training of Deep Neural Network using Model Averaging",
    "issued": {
      "date-parts": [
        [
          2018,
          7,
          3
        ]
      ]
    },
    "author": [
      {
        "given": "Hang",
        "family": "Su"
      },
      {
        "given": "Haoyu",
        "family": "Chen"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  In this work we apply model averaging to parallel training of deep neural network (DNN). Parallelization is done in a model averaging manner. Data is partitioned and distributed to different nodes for local model updates, and model averaging across nodes is done every few minibatches. We use multiple GPUs for data parallelization, and Message Passing Interface (MPI) for communication between nodes, which allows us to perform model averaging frequently without losing much time on communication. We investigate the effectiveness of Natural Gradient Stochastic Gradient Descent (NG-SGD) and Restricted Boltzmann Machine (RBM) pretraining for parallel training in model-averaging framework, and explore the best setups in term of different learning rate schedules, averaging frequencies and minibatch sizes. It is shown that NG-SGD and RBM pretraining benefits parameter-averaging based model training. On the 300h Switchboard dataset, a 9.3 times speedup is achieved using 16 GPUs and 17 times speedup using 32 GPUs with limited decoding accuracy loss. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1507.01239"
  },
  {
    "id": "Z7fd0BYf",
    "URL": "https://arxiv.org/abs/1510.02855",
    "number": "1510.02855",
    "title": "AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery",
    "issued": {
      "date-parts": [
        [
          2015,
          10,
          13
        ]
      ]
    },
    "author": [
      {
        "given": "Izhar",
        "family": "Wallach"
      },
      {
        "given": "Michael",
        "family": "Dzamba"
      },
      {
        "given": "Abraham",
        "family": "Heifets"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best predictive performance in areas such as speech and image recognition by hierarchically composing simple local features into complex models. Although DNNs have been used in drug discovery for QSAR and ligand-based bioactivity predictions, none of these models have benefited from this powerful convolutional architecture. This paper introduces AtomNet, the first structure-based, deep convolutional neural network designed to predict the bioactivity of small molecules for drug discovery applications. We demonstrate how to apply the convolutional concepts of feature locality and hierarchical composition to the modeling of bioactivity and chemical interactions. In further contrast to existing DNN techniques, we show that AtomNet's application of local convolutional filters to structural target information successfully predicts new active molecules for targets with no previously known modulators. Finally, we show that AtomNet outperforms previous docking approaches on a diverse set of benchmarks by a large margin, achieving an AUC greater than 0.9 on 57.8% of the targets in the DUDE benchmark. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1510.02855"
  },
  {
    "id": "glyI7H6F",
    "URL": "https://arxiv.org/abs/1510.07641",
    "number": "1510.07641",
    "title": "Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks",
    "issued": {
      "date-parts": [
        [
          2017,
          3,
          23
        ]
      ]
    },
    "author": [
      {
        "given": "Zachary C.",
        "family": "Lipton"
      },
      {
        "given": "David C.",
        "family": "Kale"
      },
      {
        "given": "Randall C.",
        "family": "Wetzel"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We present a novel application of LSTM recurrent neural networks to multilabel classification of diagnoses given variable-length time series of clinical measurements. Our method outperforms a strong baseline on a variety of metrics. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1510.07641"
  },
  {
    "id": "15lbUf0as",
    "URL": "https://arxiv.org/abs/1511.02386",
    "number": "1511.02386",
    "title": "Hierarchical Variational Models",
    "issued": {
      "date-parts": [
        [
          2016,
          6,
          1
        ]
      ]
    },
    "author": [
      {
        "given": "Rajesh",
        "family": "Ranganath"
      },
      {
        "given": "Dustin",
        "family": "Tran"
      },
      {
        "given": "David M.",
        "family": "Blei"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Black box variational inference allows researchers to easily prototype and evaluate an array of models. Recent advances allow such algorithms to scale to high dimensions. However, a central question remains: How to specify an expressive variational distribution that maintains efficient computation? To address this, we develop hierarchical variational models (HVMs). HVMs augment a variational approximation with a prior on its parameters, which allows it to capture complex structure for both discrete and continuous latent variables. The algorithm we develop is black box, can be used for any HVM, and has the same computational efficiency as the original approximation. We study HVMs on a variety of deep discrete latent variable models. HVMs generalize other expressive variational distributions and maintains higher fidelity to the posterior. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1511.02386"
  },
  {
    "id": "11tMRPqto",
    "URL": "https://arxiv.org/abs/1511.05942",
    "number": "1511.05942",
    "title": "Doctor AI: Predicting Clinical Events via Recurrent Neural Networks",
    "issued": {
      "date-parts": [
        [
          2016,
          9,
          29
        ]
      ]
    },
    "author": [
      {
        "given": "Edward",
        "family": "Choi"
      },
      {
        "given": "Mohammad Taha",
        "family": "Bahadori"
      },
      {
        "given": "Andy",
        "family": "Schuetz"
      },
      {
        "given": "Walter F.",
        "family": "Stewart"
      },
      {
        "given": "Jimeng",
        "family": "Sun"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses. Doctor AI is a temporal model using recurrent neural networks (RNN) and was developed and applied to longitudinal time stamped EHR data from 260K patients over 8 years. Encounter records (e.g. diagnosis codes, medication codes or procedure codes) were input to RNN to predict (all) the diagnosis and medication categories for a subsequent visit. Doctor AI assesses the history of patients to make multilabel predictions (one label for each diagnosis or medication category). Based on separate blind test set evaluation, Doctor AI can perform differential diagnosis with up to 79% recall@30, significantly higher than several baselines. Moreover, we demonstrate great generalizability of Doctor AI by adapting the resulting models from one institution to another without losing substantial accuracy. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1511.05942"
  },
  {
    "id": "rmJZ2Aui",
    "URL": "https://arxiv.org/abs/1511.06051",
    "number": "1511.06051",
    "title": "SparkNet: Training Deep Networks in Spark",
    "issued": {
      "date-parts": [
        [
          2016,
          3,
          1
        ]
      ]
    },
    "author": [
      {
        "given": "Philipp",
        "family": "Moritz"
      },
      {
        "given": "Robert",
        "family": "Nishihara"
      },
      {
        "given": "Ion",
        "family": "Stoica"
      },
      {
        "given": "Michael I.",
        "family": "Jordan"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. However, widely-popular batch-processing computational frameworks like MapReduce and Spark were not designed to support the asynchronous and communication-intensive workloads of existing distributed deep learning systems. We introduce SparkNet, a framework for training deep networks in Spark. Our implementation includes a convenient interface for reading data from Spark RDDs, a Scala interface to the Caffe deep learning framework, and a lightweight multi-dimensional tensor library. Using a simple parallelization scheme for stochastic gradient descent, SparkNet scales well with the cluster size and tolerates very high-latency communication. Furthermore, it is easy to deploy and use with no parameter tuning, and it is compatible with existing Caffe models. We quantify the dependence of the speedup obtained by SparkNet on the number of machines, the communication frequency, and the cluster's communication overhead, and we benchmark our system's performance on the ImageNet dataset. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1511.06051"
  },
  {
    "id": "136QC0Zul",
    "URL": "https://arxiv.org/abs/1511.06434v2",
    "number": "1511.06434v2",
    "version": "v2",
    "title": "Unsupervised Representation Learning with Deep Convolutional Generative\n  Adversarial Networks",
    "issued": {
      "date-parts": [
        [
          2015,
          11,
          19
        ]
      ]
    },
    "author": [
      {
        "literal": "Alec Radford"
      },
      {
        "literal": "Luke Metz"
      },
      {
        "literal": "Soumith Chintala"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks - demonstrating their applicability as general image representations.",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1511.06434v2"
  },
  {
    "id": "14DAmZTDg",
    "URL": "https://arxiv.org/abs/1512.03542",
    "number": "1512.03542",
    "title": "Distilling Knowledge from Deep Networks with Applications to Healthcare Domain",
    "issued": {
      "date-parts": [
        [
          2015,
          12,
          14
        ]
      ]
    },
    "author": [
      {
        "given": "Zhengping",
        "family": "Che"
      },
      {
        "given": "Sanjay",
        "family": "Purushotham"
      },
      {
        "given": "Robinder",
        "family": "Khemani"
      },
      {
        "given": "Yan",
        "family": "Liu"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research. Deep Learning models have shown superior performance for robust prediction in computational phenotyping tasks, but suffer from the issue of model interpretability which is crucial for clinicians involved in decision-making. In this paper, we introduce a novel knowledge-distillation approach called Interpretable Mimic Learning, to learn interpretable phenotype features for making robust prediction while mimicking the performance of deep learning models. Our framework uses Gradient Boosting Trees to learn interpretable features from deep learning models such as Stacked Denoising Autoencoder and Long Short-Term Memory. Exhaustive experiments on a real-world clinical time-series dataset show that our method obtains similar or better performance than the deep learning models, and it provides interpretable phenotypes for clinical decision making. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1512.03542"
  },
  {
    "id": "HRXii6Ni",
    "URL": "https://arxiv.org/abs/1602.00357",
    "number": "1602.00357",
    "title": "DeepCare: A Deep Dynamic Memory Model for Predictive Medicine",
    "issued": {
      "date-parts": [
        [
          2017,
          4,
          12
        ]
      ]
    },
    "author": [
      {
        "given": "Trang",
        "family": "Pham"
      },
      {
        "given": "Truyen",
        "family": "Tran"
      },
      {
        "given": "Dinh",
        "family": "Phung"
      },
      {
        "given": "Svetha",
        "family": "Venkatesh"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1602.00357"
  },
  {
    "id": "9NKsJjSw",
    "URL": "https://arxiv.org/abs/1602.04283",
    "number": "1602.04283",
    "title": "Deep Learning on FPGAs: Past, Present, and Future",
    "issued": {
      "date-parts": [
        [
          2016,
          2,
          16
        ]
      ]
    },
    "author": [
      {
        "given": "Griffin",
        "family": "Lacey"
      },
      {
        "given": "Graham W.",
        "family": "Taylor"
      },
      {
        "given": "Shawki",
        "family": "Areibi"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  The rapid growth of data size and accessibility in recent years has instigated a shift of philosophy in algorithm design for artificial intelligence. Instead of engineering algorithms by hand, the ability to learn composable systems automatically from massive amounts of data has led to ground-breaking performance in important domains such as computer vision, speech recognition, and natural language processing. The most popular class of techniques used in these domains is called deep learning, and is seeing significant attention from industry. However, these models require incredible amounts of data and compute power to train, and are limited by the need for better hardware acceleration to accommodate scaling beyond current data and model sizes. While the current solution has been to use clusters of graphics processing units (GPU) as general purpose processors (GPGPU), the use of field programmable gate arrays (FPGA) provide an interesting alternative. Current trends in design tools for FPGAs have made them more compatible with the high-level software practices typically practiced in the deep learning community, making FPGAs more accessible to those who build and deploy models. Since FPGA architectures are flexible, this could also allow researchers the ability to explore model-level optimizations beyond what is possible on fixed architectures such as GPUs. As well, FPGAs tend to provide high performance per watt of power consumption, which is of particular importance for application scientists interested in large scale server-based deployment or resource-limited embedded applications. This review takes a look at deep learning and FPGAs from a hardware acceleration perspective, identifying trends and innovations that make these technologies a natural fit, and motivates a discussion on how FPGAs may best serve the needs of the deep learning community moving forward. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1602.04283"
  },
  {
    "id": "QwXSJhr0",
    "URL": "https://arxiv.org/abs/1602.04938",
    "number": "1602.04938",
    "title": "\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier",
    "issued": {
      "date-parts": [
        [
          2016,
          8,
          10
        ]
      ]
    },
    "author": [
      {
        "given": "Marco Tulio",
        "family": "Ribeiro"
      },
      {
        "given": "Sameer",
        "family": "Singh"
      },
      {
        "given": "Carlos",
        "family": "Guestrin"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one. In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1602.04938"
  },
  {
    "id": "hOeUlCvS",
    "URL": "https://arxiv.org/abs/1603.04467",
    "number": "1603.04467",
    "title": "TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems",
    "issued": {
      "date-parts": [
        [
          2016,
          3,
          17
        ]
      ]
    },
    "author": [
      {
        "given": "Martín",
        "family": "Abadi"
      },
      {
        "given": "Ashish",
        "family": "Agarwal"
      },
      {
        "given": "Paul",
        "family": "Barham"
      },
      {
        "given": "Eugene",
        "family": "Brevdo"
      },
      {
        "given": "Zhifeng",
        "family": "Chen"
      },
      {
        "given": "Craig",
        "family": "Citro"
      },
      {
        "given": "Greg S.",
        "family": "Corrado"
      },
      {
        "given": "Andy",
        "family": "Davis"
      },
      {
        "given": "Jeffrey",
        "family": "Dean"
      },
      {
        "given": "Matthieu",
        "family": "Devin"
      },
      {
        "given": "Sanjay",
        "family": "Ghemawat"
      },
      {
        "given": "Ian",
        "family": "Goodfellow"
      },
      {
        "given": "Andrew",
        "family": "Harp"
      },
      {
        "given": "Geoffrey",
        "family": "Irving"
      },
      {
        "given": "Michael",
        "family": "Isard"
      },
      {
        "given": "Yangqing",
        "family": "Jia"
      },
      {
        "given": "Rafal",
        "family": "Jozefowicz"
      },
      {
        "given": "Lukasz",
        "family": "Kaiser"
      },
      {
        "given": "Manjunath",
        "family": "Kudlur"
      },
      {
        "given": "Josh",
        "family": "Levenberg"
      },
      {
        "given": "Dan",
        "family": "Mane"
      },
      {
        "given": "Rajat",
        "family": "Monga"
      },
      {
        "given": "Sherry",
        "family": "Moore"
      },
      {
        "given": "Derek",
        "family": "Murray"
      },
      {
        "given": "Chris",
        "family": "Olah"
      },
      {
        "given": "Mike",
        "family": "Schuster"
      },
      {
        "given": "Jonathon",
        "family": "Shlens"
      },
      {
        "given": "Benoit",
        "family": "Steiner"
      },
      {
        "given": "Ilya",
        "family": "Sutskever"
      },
      {
        "given": "Kunal",
        "family": "Talwar"
      },
      {
        "given": "Paul",
        "family": "Tucker"
      },
      {
        "given": "Vincent",
        "family": "Vanhoucke"
      },
      {
        "given": "Vijay",
        "family": "Vasudevan"
      },
      {
        "given": "Fernanda",
        "family": "Viegas"
      },
      {
        "given": "Oriol",
        "family": "Vinyals"
      },
      {
        "given": "Pete",
        "family": "Warden"
      },
      {
        "given": "Martin",
        "family": "Wattenberg"
      },
      {
        "given": "Martin",
        "family": "Wicke"
      },
      {
        "given": "Yuan",
        "family": "Yu"
      },
      {
        "given": "Xiaoqiang",
        "family": "Zheng"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at www.tensorflow.org. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1603.04467"
  },
  {
    "id": "1GwC1ll6h",
    "URL": "https://arxiv.org/abs/1603.09123v2",
    "number": "1603.09123v2",
    "version": "v2",
    "title": "deepTarget: End-to-end Learning Framework for microRNA Target Prediction\n  using Deep Recurrent Neural Networks",
    "issued": {
      "date-parts": [
        [
          2016,
          3,
          30
        ]
      ]
    },
    "author": [
      {
        "literal": "Byunghan Lee"
      },
      {
        "literal": "Junghwan Baek"
      },
      {
        "literal": "Seunghyun Park"
      },
      {
        "literal": "Sungroh Yoon"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "MicroRNAs (miRNAs) are short sequences of ribonucleic acids that control the expression of target messenger RNAs (mRNAs) by binding them. Robust prediction of miRNA-mRNA pairs is of utmost importance in deciphering gene regulations but has been challenging because of high false positive rates, despite a deluge of computational tools that normally require laborious manual feature extraction. This paper presents an end-to-end machine learning framework for miRNA target prediction. Leveraged by deep recurrent neural networks-based auto-encoding and sequence-sequence interaction learning, our approach not only delivers an unprecedented level of accuracy but also eliminates the need for manual feature extraction. The performance gap between the proposed method and existing alternatives is substantial (over 25% increase in F-measure), and deepTarget delivers a quantum leap in the long-standing challenge of robust miRNA target prediction.",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1603.09123v2"
  },
  {
    "id": "AEc66xxR",
    "URL": "https://arxiv.org/abs/1604.07043",
    "number": "1604.07043",
    "title": "Towards Better Analysis of Deep Convolutional Neural Networks",
    "issued": {
      "date-parts": [
        [
          2016,
          5,
          5
        ]
      ]
    },
    "author": [
      {
        "given": "Mengchen",
        "family": "Liu"
      },
      {
        "given": "Jiaxin",
        "family": "Shi"
      },
      {
        "given": "Zhen",
        "family": "Li"
      },
      {
        "given": "Chongxuan",
        "family": "Li"
      },
      {
        "given": "Jun",
        "family": "Zhu"
      },
      {
        "given": "Shixia",
        "family": "Liu"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification. However, the development of high-quality deep models typically relies on a substantial amount of trial-and-error, as there is still no clear understanding of when and why a deep model works. In this paper, we present a visual analytics approach for better understanding, diagnosing, and refining deep CNNs. We formulate a deep CNN as a directed acyclic graph. Based on this formulation, a hybrid visualization is developed to disclose the multiple facets of each neuron and the interactions between them. In particular, we introduce a hierarchical rectangle packing algorithm and a matrix reordering algorithm to show the derived features of a neuron cluster. We also propose a biclustering-based edge bundling method to reduce visual clutter caused by a large number of connections between neurons. We evaluated our method on a set of CNNs and the results are generally favorable. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1604.07043"
  },
  {
    "id": "1TeyWffV",
    "URL": "https://arxiv.org/abs/1605.00017",
    "number": "1605.00017",
    "title": "deepMiRGene: Deep Neural Network based Precursor microRNA Prediction",
    "issued": {
      "date-parts": [
        [
          2016,
          5,
          4
        ]
      ]
    },
    "author": [
      {
        "given": "Seunghyun",
        "family": "Park"
      },
      {
        "given": "Seonwoo",
        "family": "Min"
      },
      {
        "given": "Hyunsoo",
        "family": "Choi"
      },
      {
        "given": "Sungroh",
        "family": "Yoon"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Since microRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation, miRNA identification is one of the most essential problems in computational biology. miRNAs are usually short in length ranging between 20 and 23 base pairs. It is thus often difficult to distinguish miRNA-encoding sequences from other non-coding RNAs and pseudo miRNAs that have a similar length, and most previous studies have recommended using precursor miRNAs instead of mature miRNAs for robust detection. A great number of conventional machine-learning-based classification methods have been proposed, but they often have the serious disadvantage of requiring manual feature engineering, and their performance is limited as well. In this paper, we propose a novel miRNA precursor prediction algorithm, deepMiRGene, based on recurrent neural networks, specifically long short-term memory networks. deepMiRGene automatically learns suitable features from the data themselves without manual feature engineering and constructs a model that can successfully reflect structural characteristics of precursor miRNAs. For the performance evaluation of our approach, we have employed several widely used evaluation metrics on three recent benchmark datasets and verified that deepMiRGene delivered comparable performance among the current state-of-the-art tools. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1605.00017"
  },
  {
    "id": "173ftiSzF",
    "URL": "https://arxiv.org/abs/1605.03661",
    "number": "1605.03661",
    "title": "Learning Representations for Counterfactual Inference",
    "issued": {
      "date-parts": [
        [
          2018,
          6,
          7
        ]
      ]
    },
    "author": [
      {
        "given": "Fredrik D.",
        "family": "Johansson"
      },
      {
        "given": "Uri",
        "family": "Shalit"
      },
      {
        "given": "David",
        "family": "Sontag"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We consider the task of answering counterfactual questions such as, \"Would this patient have lower blood sugar had she received a different medication?\". We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Our deep learning algorithm significantly outperforms the previous state-of-the-art. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1605.03661"
  },
  {
    "id": "SAvEOARL",
    "URL": "https://arxiv.org/abs/1605.07156",
    "number": "1605.07156",
    "title": "Genetic Architect: Discovering Genomic Structure with Learned Neural Architectures",
    "issued": {
      "date-parts": [
        [
          2016,
          5,
          24
        ]
      ]
    },
    "author": [
      {
        "given": "Laura",
        "family": "Deming"
      },
      {
        "given": "Sasha",
        "family": "Targ"
      },
      {
        "given": "Nate",
        "family": "Sauder"
      },
      {
        "given": "Diogo",
        "family": "Almeida"
      },
      {
        "given": "Chun Jimmie",
        "family": "Ye"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Each human genome is a 3 billion base pair set of encoding instructions. Decoding the genome using deep learning fundamentally differs from most tasks, as we do not know the full structure of the data and therefore cannot design architectures to suit it. As such, architectures that fit the structure of genomics should be learned not prescribed. Here, we develop a novel search algorithm, applicable across domains, that discovers an optimal architecture which simultaneously learns general genomic patterns and identifies the most important sequence motifs in predicting functional genomic outcomes. The architectures we find using this algorithm succeed at using only RNA expression data to predict gene regulatory structure, learn human-interpretable visualizations of key sequence motifs, and surpass state-of-the-art results on benchmark genomics challenges. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1605.07156"
  },
  {
    "id": "5Il3kN32",
    "URL": "https://arxiv.org/abs/1605.07723",
    "number": "1605.07723",
    "title": "Data Programming: Creating Large Training Sets, Quickly",
    "issued": {
      "date-parts": [
        [
          2018,
          12,
          10
        ]
      ]
    },
    "author": [
      {
        "given": "Alexander",
        "family": "Ratner"
      },
      {
        "given": "Christopher",
        "family": "De Sa"
      },
      {
        "given": "Sen",
        "family": "Wu"
      },
      {
        "given": "Daniel",
        "family": "Selsam"
      },
      {
        "given": "Christopher",
        "family": "Ré"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques. For some applications, creating labeled training sets is the most time-consuming and expensive part of applying machine learning. We therefore propose a paradigm for the programmatic creation of training sets called data programming in which users express weak supervision strategies or domain heuristics as labeling functions, which are programs that label subsets of the data, but that are noisy and may conflict. We show that by explicitly representing this training set labeling process as a generative model, we can \"denoise\" the generated training set, and establish theoretically that we can recover the parameters of these generative models in a handful of settings. We then show how to modify a discriminative loss function to make it noise-aware, and demonstrate our method over a range of discriminative models including logistic regression and LSTMs. Experimentally, on the 2014 TAC-KBP Slot Filling challenge, we show that data programming would have led to a new winning score, and also show that applying data programming to an LSTM model leads to a TAC-KBP score almost 6 F1 points over a state-of-the-art LSTM baseline (and into second place in the competition). Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1605.07723"
  },
  {
    "id": "JUF9VoRD",
    "URL": "https://arxiv.org/abs/1606.00575",
    "number": "1606.00575",
    "title": "Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks",
    "issued": {
      "date-parts": [
        [
          2017,
          7,
          19
        ]
      ]
    },
    "author": [
      {
        "given": "Shizhao",
        "family": "Sun"
      },
      {
        "given": "Wei",
        "family": "Chen"
      },
      {
        "given": "Jiang",
        "family": "Bian"
      },
      {
        "given": "Xiaoguang",
        "family": "Liu"
      },
      {
        "given": "Tie-Yan",
        "family": "Liu"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Parallelization framework has become a necessity to speed up the training of deep neural networks (DNN) recently. Such framework typically employs the Model Average approach, denoted as MA-DNN, in which parallel workers conduct respective training based on their own local data while the parameters of local models are periodically communicated and averaged to obtain a global model which serves as the new start of local models. However, since DNN is a highly non-convex model, averaging parameters cannot ensure that such global model can perform better than those local models. To tackle this problem, we introduce a new parallel training framework called Ensemble-Compression, denoted as EC-DNN. In this framework, we propose to aggregate the local models by ensemble, i.e., averaging the outputs of local models instead of the parameters. As most of prevalent loss functions are convex to the output of DNN, the performance of ensemble-based global model is guaranteed to be at least as good as the average performance of local models. However, a big challenge lies in the explosion of model size since each round of ensemble can give rise to multiple times size increment. Thus, we carry out model compression after each ensemble, specialized by a distillation based method in this paper, to reduce the size of the global model to be the same as the local ones. Our experimental results demonstrate the prominent advantage of EC-DNN over MA-DNN in terms of both accuracy and speedup. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1606.00575"
  },
  {
    "id": "1FE0F2pQ",
    "URL": "https://arxiv.org/abs/1606.00931",
    "number": "1606.00931",
    "title": "DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network",
    "issued": {
      "date-parts": [
        [
          2018,
          3,
          9
        ]
      ]
    },
    "author": [
      {
        "given": "Jared",
        "family": "Katzman"
      },
      {
        "given": "Uri",
        "family": "Shaham"
      },
      {
        "given": "Jonathan",
        "family": "Bates"
      },
      {
        "given": "Alexander",
        "family": "Cloninger"
      },
      {
        "given": "Tingting",
        "family": "Jiang"
      },
      {
        "given": "Yuval",
        "family": "Kluger"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems. We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations. We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient's covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient's features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it's personalized treatment recommendations would increase the survival time of a set of patients. The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of failure. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1606.00931",
    "DOI": "10.1186/s12874-018-0482-1"
  },
  {
    "id": "O7Vbecm2",
    "URL": "https://arxiv.org/abs/1606.01865",
    "number": "1606.01865",
    "title": "Recurrent Neural Networks for Multivariate Time Series with Missing Values",
    "issued": {
      "date-parts": [
        [
          2016,
          11,
          8
        ]
      ]
    },
    "author": [
      {
        "given": "Zhengping",
        "family": "Che"
      },
      {
        "given": "Sanjay",
        "family": "Purushotham"
      },
      {
        "given": "Kyunghyun",
        "family": "Cho"
      },
      {
        "given": "David",
        "family": "Sontag"
      },
      {
        "given": "Yan",
        "family": "Liu"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provides useful insights for better understanding and utilization of missing values in time series analysis. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1606.01865"
  },
  {
    "id": "4zpZxjHR",
    "URL": "https://arxiv.org/abs/1606.04130",
    "number": "1606.04130",
    "title": "Modeling Missing Data in Clinical Time Series with RNNs",
    "issued": {
      "date-parts": [
        [
          2016,
          11,
          14
        ]
      ]
    },
    "author": [
      {
        "given": "Zachary C.",
        "family": "Lipton"
      },
      {
        "given": "David C.",
        "family": "Kale"
      },
      {
        "given": "Randall",
        "family": "Wetzel"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the pediatric intensive care unit (PICU) at Children's Hospital Los Angeles, our data consists of multivariate time series of observations. The measurements are irregularly spaced, leading to missingness patterns in temporally discretized sequences. While these artifacts are typically handled by imputation, we achieve superior predictive performance by treating the artifacts as features. Unlike linear models, recurrent neural networks can realize this improvement using only simple binary indicators of missingness. For linear models, we show an alternative strategy to capture this signal. Training models on missingness patterns only, we show that for some diseases, what tests are run can be as predictive as the results themselves. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1606.04130"
  },
  {
    "id": "ZUCVI5eU",
    "URL": "https://arxiv.org/abs/1606.04155",
    "number": "1606.04155",
    "title": "Rationalizing Neural Predictions",
    "issued": {
      "date-parts": [
        [
          2016,
          11,
          4
        ]
      ]
    },
    "author": [
      {
        "given": "Tao",
        "family": "Lei"
      },
      {
        "given": "Regina",
        "family": "Barzilay"
      },
      {
        "given": "Tommi",
        "family": "Jaakkola"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Prediction without justification has limited applicability. As a remedy, we learn to extract pieces of input text as justifications -- rationales -- that are tailored to be short and coherent, yet sufficient for making the same prediction. Our approach combines two modular components, generator and encoder, which are trained to operate well together. The generator specifies a distribution over text fragments as candidate rationales and these are passed through the encoder for prediction. Rationales are never given during training. Instead, the model is regularized by desiderata for rationales. We evaluate the approach on multi-aspect sentiment analysis against manually annotated test cases. Our approach outperforms attention-based baseline by a significant margin. We also successfully illustrate the method on the question retrieval task. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1606.04155"
  },
  {
    "id": "mbEp6jNr",
    "URL": "https://arxiv.org/abs/1606.05718",
    "number": "1606.05718",
    "title": "Deep Learning for Identifying Metastatic Breast Cancer",
    "issued": {
      "date-parts": [
        [
          2016,
          6,
          21
        ]
      ]
    },
    "author": [
      {
        "given": "Dayong",
        "family": "Wang"
      },
      {
        "given": "Aditya",
        "family": "Khosla"
      },
      {
        "given": "Rishab",
        "family": "Gargeya"
      },
      {
        "given": "Humayun",
        "family": "Irshad"
      },
      {
        "given": "Andrew H.",
        "family": "Beck"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to evaluate computational systems for the automated detection of metastatic breast cancer in whole slide images of sentinel lymph node biopsies. Our team won both competitions in the grand challenge, obtaining an area under the receiver operating curve (AUC) of 0.925 for the task of whole slide image classification and a score of 0.7051 for the tumor localization task. A pathologist independently reviewed the same images, obtaining a whole slide image classification AUC of 0.966 and a tumor localization score of 0.733. Combining our deep learning system's predictions with the human pathologist's diagnoses increased the pathologist's AUC to 0.995, representing an approximately 85 percent reduction in human error rate. These results demonstrate the power of using deep learning to produce significant improvements in the accuracy of pathological diagnoses. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1606.05718"
  },
  {
    "id": "InL72p4N",
    "URL": "https://arxiv.org/abs/1606.06565",
    "number": "1606.06565",
    "title": "Concrete Problems in AI Safety",
    "issued": {
      "date-parts": [
        [
          2016,
          7,
          26
        ]
      ]
    },
    "author": [
      {
        "given": "Dario",
        "family": "Amodei"
      },
      {
        "given": "Chris",
        "family": "Olah"
      },
      {
        "given": "Jacob",
        "family": "Steinhardt"
      },
      {
        "given": "Paul",
        "family": "Christiano"
      },
      {
        "given": "John",
        "family": "Schulman"
      },
      {
        "given": "Dan",
        "family": "Mané"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. In this paper we discuss one such potential impact: the problem of accidents in machine learning systems, defined as unintended and harmful behavior that may emerge from poor design of real-world AI systems. We present a list of five practical research problems related to accident risk, categorized according to whether the problem originates from having the wrong objective function (\"avoiding side effects\" and \"avoiding reward hacking\"), an objective function that is too expensive to evaluate frequently (\"scalable supervision\"), or undesirable behavior during the learning process (\"safe exploration\" and \"distributional shift\"). We review previous work in these areas as well as suggesting research directions with a focus on relevance to cutting-edge AI systems. Finally, we consider the high-level question of how to think most productively about the safety of forward-looking applications of AI. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1606.06565"
  },
  {
    "id": "1Ad3UOefc",
    "URL": "https://arxiv.org/abs/1606.07461",
    "number": "1606.07461",
    "title": "LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks",
    "issued": {
      "date-parts": [
        [
          2017,
          10,
          31
        ]
      ]
    },
    "author": [
      {
        "given": "Hendrik",
        "family": "Strobelt"
      },
      {
        "given": "Sebastian",
        "family": "Gehrmann"
      },
      {
        "given": "Hanspeter",
        "family": "Pfister"
      },
      {
        "given": "Alexander M.",
        "family": "Rush"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noise. In this work, we present LSTMVIS, a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows users to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with structural annotations from their domain. We show several use cases of the tool for analyzing specific hidden state properties on dataset containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis. We characterize the domain, the different stakeholders, and their goals and tasks. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1606.07461"
  },
  {
    "id": "uP7SgBVd",
    "URL": "https://arxiv.org/abs/1606.08793",
    "number": "1606.08793",
    "title": "Modeling Industrial ADMET Data with Multitask Networks",
    "issued": {
      "date-parts": [
        [
          2017,
          1,
          16
        ]
      ]
    },
    "author": [
      {
        "given": "Steven",
        "family": "Kearnes"
      },
      {
        "given": "Brian",
        "family": "Goldman"
      },
      {
        "given": "Vijay",
        "family": "Pande"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Deep learning methods such as multitask neural networks have recently been applied to ligand-based virtual screening and other drug discovery applications. Using a set of industrial ADMET datasets, we compare neural networks to standard baseline models and analyze multitask learning effects with both random cross-validation and a more relevant temporal validation scheme. We confirm that multitask learning can provide modest benefits over single-task models and show that smaller datasets tend to benefit more than larger datasets from multitask learning. Additionally, we find that adding massive amounts of side information is not guaranteed to improve performance relative to simpler multitask learning. Our results emphasize that multitask effects are highly dataset-dependent, suggesting the use of dataset-specific models to maximize overall performance. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1606.08793"
  },
  {
    "id": "7yE9K08a",
    "URL": "https://arxiv.org/abs/1606.08813v3",
    "number": "1606.08813v3",
    "version": "v3",
    "title": "European Union regulations on algorithmic decision-making and a \"right\n  to explanation\"",
    "issued": {
      "date-parts": [
        [
          2016,
          6,
          28
        ]
      ]
    },
    "author": [
      {
        "literal": "Bryce Goodman"
      },
      {
        "literal": "Seth Flaxman"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "We summarize the potential impact that the European Union's new General Data Protection Regulation will have on the routine use of machine learning algorithms. Slated to take effect as law across the EU in 2018, it will restrict automated individual decision-making (that is, algorithms that make decisions based on user-level predictors) which \"significantly affect\" users. The law will also effectively create a \"right to explanation,\" whereby a user can ask for an explanation of an algorithmic decision that was made about them. We argue that while this law will pose large challenges for industry, it highlights opportunities for computer scientists to take the lead in designing algorithms and evaluation frameworks which avoid discrimination and enable explanation.",
    "DOI": "10.1609/aimag.v38i3.2741",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1606.08813v3"
  },
  {
    "id": "G10wkFHt",
    "URL": "https://arxiv.org/abs/1607.02078",
    "number": "1607.02078",
    "title": "DeepChrome: Deep-learning for predicting gene expression from histone modifications",
    "issued": {
      "date-parts": [
        [
          2016,
          7,
          8
        ]
      ]
    },
    "author": [
      {
        "given": "Ritambhara",
        "family": "Singh"
      },
      {
        "given": "Jack",
        "family": "Lanchantin"
      },
      {
        "given": "Gabriel",
        "family": "Robins"
      },
      {
        "given": "Yanjun",
        "family": "Qi"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Motivation: Histone modifications are among the most important factors that control gene regulation. Computational methods that predict gene expression from histone modification signals are highly desirable for understanding their combinatorial effects in gene regulation. This knowledge can help in developing 'epigenetic drugs' for diseases like cancer. Previous studies for quantifying the relationship between histone modifications and gene expression levels either failed to capture combinatorial effects or relied on multiple methods that separate predictions and combinatorial analysis. This paper develops a unified discriminative framework using a deep convolutional neural network to classify gene expression using histone modification data as input. Our system, called DeepChrome, allows automatic extraction of complex interactions among important features. To simultaneously visualize the combinatorial interactions among histone modifications, we propose a novel optimization-based technique that generates feature pattern maps from the learnt deep model. This provides an intuitive description of underlying epigenetic mechanisms that regulate genes. Results: We show that DeepChrome outperforms state-of-the-art models like Support Vector Machines and Random Forests for gene expression classification task on 56 different cell-types from REMC database. The output of our visualization technique not only validates the previous observations but also allows novel insights about combinatorial interactions among histone modification marks, some of which have recently been observed by experimental studies. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1607.02078"
  },
  {
    "id": "Ohd1Q9Xw",
    "URL": "https://arxiv.org/abs/1607.07519",
    "number": "1607.07519",
    "title": "Deepr: A Convolutional Net for Medical Records",
    "issued": {
      "date-parts": [
        [
          2016,
          7,
          27
        ]
      ]
    },
    "author": [
      {
        "given": "Phuoc",
        "family": "Nguyen"
      },
      {
        "given": "Truyen",
        "family": "Tran"
      },
      {
        "given": "Nilmini",
        "family": "Wickramasinghe"
      },
      {
        "given": "Svetha",
        "family": "Venkatesh"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1607.07519"
  },
  {
    "id": "c6MfDdWP",
    "URL": "https://arxiv.org/abs/1608.00647",
    "number": "1608.00647",
    "title": "Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests",
    "issued": {
      "date-parts": [
        [
          2016,
          9,
          22
        ]
      ]
    },
    "author": [
      {
        "given": "Narges",
        "family": "Razavian"
      },
      {
        "given": "Jake",
        "family": "Marcus"
      },
      {
        "given": "David",
        "family": "Sontag"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Disparate areas of machine learning have benefited from models that can take raw data with little preprocessing as input and learn rich representations of that raw data in order to perform well on a given prediction task. We evaluate this approach in healthcare by using longitudinal measurements of lab tests, one of the more raw signals of a patient's health state widely available in clinical data, to predict disease onsets. In particular, we train a Long Short-Term Memory (LSTM) recurrent neural network and two novel convolutional neural networks for multi-task prediction of disease onset for 133 conditions based on 18 common lab tests measured over time in a cohort of 298K patients derived from 8 years of administrative claims data. We compare the neural networks to a logistic regression with several hand-engineered, clinically relevant features. We find that the representation-based learning approaches significantly outperform this baseline. We believe that our work suggests a new avenue for patient risk stratification based solely on lab results. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1608.00647"
  },
  {
    "id": "qXdO2aMm",
    "URL": "https://arxiv.org/abs/1608.02158",
    "number": "1608.02158",
    "title": "Deep Survival Analysis",
    "issued": {
      "date-parts": [
        [
          2016,
          9,
          20
        ]
      ]
    },
    "author": [
      {
        "given": "Rajesh",
        "family": "Ranganath"
      },
      {
        "given": "Adler",
        "family": "Perotte"
      },
      {
        "given": "Noémie",
        "family": "Elhadad"
      },
      {
        "given": "David",
        "family": "Blei"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  The electronic health record (EHR) provides an unprecedented opportunity to build actionable tools to support physicians at the point of care. In this paper, we investigate survival analysis in the context of EHR data. We introduce deep survival analysis, a hierarchical generative approach to survival analysis. It departs from previous approaches in two primary ways: (1) all observations, including covariates, are modeled jointly conditioned on a rich latent structure; and (2) the observations are aligned by their failure time, rather than by an arbitrary time zero as in traditional survival analysis. Further, it (3) scalably handles heterogeneous (continuous and discrete) data types that occur in the EHR. We validate deep survival analysis model by stratifying patients according to risk of developing coronary heart disease (CHD). Specifically, we study a dataset of 313,000 patients corresponding to 5.5 million months of observations. When compared to the clinically validated Framingham CHD risk score, deep survival analysis is significantly superior in stratifying patients according to their risk. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1608.02158"
  },
  {
    "id": "Dwi2eAvT",
    "URL": "https://arxiv.org/abs/1608.03644",
    "number": "1608.03644",
    "title": "Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks",
    "issued": {
      "date-parts": [
        [
          2016,
          10,
          20
        ]
      ]
    },
    "author": [
      {
        "given": "Jack",
        "family": "Lanchantin"
      },
      {
        "given": "Ritambhara",
        "family": "Singh"
      },
      {
        "given": "Beilun",
        "family": "Wang"
      },
      {
        "given": "Yanjun",
        "family": "Qi"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequence's saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1608.03644"
  },
  {
    "id": "UcRbawKo",
    "URL": "https://arxiv.org/abs/1608.05745",
    "number": "1608.05745",
    "title": "RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism",
    "issued": {
      "date-parts": [
        [
          2017,
          2,
          28
        ]
      ]
    },
    "author": [
      {
        "given": "Edward",
        "family": "Choi"
      },
      {
        "given": "Mohammad Taha",
        "family": "Bahadori"
      },
      {
        "given": "Joshua A.",
        "family": "Kulas"
      },
      {
        "given": "Andy",
        "family": "Schuetz"
      },
      {
        "given": "Walter F.",
        "family": "Stewart"
      },
      {
        "given": "Jimeng",
        "family": "Sun"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Accuracy and interpretability are two dominant features of successful predictive models. Typically, a choice must be made in favor of complex black box models such as recurrent neural networks (RNN) for accuracy versus less accurate but more interpretable traditional models such as logistic regression. This tradeoff poses challenges in medicine where both accuracy and interpretability are important. We addressed this challenge by developing the REverse Time AttentIoN model (RETAIN) for application to Electronic Health Records (EHR) data. RETAIN achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that recent clinical visits are likely to receive higher attention. RETAIN was tested on a large health system EHR dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as RNN, and ease of interpretability comparable to traditional models. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1608.05745"
  },
  {
    "id": "bBG5t78u",
    "URL": "https://arxiv.org/abs/1608.08225v3",
    "number": "1608.08225v3",
    "version": "v3",
    "title": "Why does deep and cheap learning work so well?",
    "issued": {
      "date-parts": [
        [
          2016,
          8,
          29
        ]
      ]
    },
    "author": [
      {
        "literal": "Henry W. Lin"
      },
      {
        "literal": "Max Tegmark"
      },
      {
        "literal": "David Rolnick"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "We show how the success of deep learning could depend not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can frequently be approximated through \"cheap learning\" with exponentially fewer parameters than generic ones. We explore how properties frequently encountered in physics such as symmetry, locality, compositionality, and polynomial log-probability translate into exceptionally simple neural networks. We further argue that when the statistical process generating the data is of a certain hierarchical form prevalent in physics and machine-learning, a deep neural network can be more efficient than a shallow one. We formalize these claims using information theory and discuss the relation to the renormalization group. We prove various \"no-flattening theorems\" showing when efficient linear deep networks cannot be accurately approximated by shallow ones without efficiency loss, for example, we show that $n$ variables cannot be multiplied using fewer than 2^n neurons in a single hidden layer.",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1608.08225v3"
  },
  {
    "id": "ULSPV0rh",
    "URL": "https://arxiv.org/abs/1609.02943",
    "number": "1609.02943",
    "title": "Stealing Machine Learning Models via Prediction APIs",
    "issued": {
      "date-parts": [
        [
          2016,
          10,
          4
        ]
      ]
    },
    "author": [
      {
        "given": "Florian",
        "family": "Tramèr"
      },
      {
        "given": "Fan",
        "family": "Zhang"
      },
      {
        "given": "Ari",
        "family": "Juels"
      },
      {
        "given": "Michael K.",
        "family": "Reiter"
      },
      {
        "given": "Thomas",
        "family": "Ristenpart"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML models are being deployed with publicly accessible query interfaces. ML-as-a-service (\"predictive analytics\") systems are an example: Some allow users to train models on potentially sensitive data and charge others for access on a pay-per-query basis.\n  The tension between model confidentiality and public access motivates our investigation of model extraction attacks. In such attacks, an adversary with black-box access, but no prior knowledge of an ML model's parameters or training data, aims to duplicate the functionality of (i.e., \"steal\") the model. Unlike in classical learning theory settings, ML-as-a-service offerings may accept partial feature vectors as inputs and include confidence values with predictions. Given these practices, we show simple, efficient attacks that extract target ML models with near-perfect fidelity for popular model classes including logistic regression, neural networks, and decision trees. We demonstrate these attacks against the online services of BigML and Amazon Machine Learning. We further show that the natural countermeasure of omitting confidence values from model outputs still admits potentially harmful model extraction attacks. Our results highlight the need for careful ML model deployment and new model extraction countermeasures. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1609.02943"
  },
  {
    "id": "1GUizyE8e",
    "URL": "https://arxiv.org/abs/1609.07061",
    "number": "1609.07061",
    "title": "Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations",
    "issued": {
      "date-parts": [
        [
          2016,
          9,
          23
        ]
      ]
    },
    "author": [
      {
        "given": "Itay",
        "family": "Hubara"
      },
      {
        "given": "Matthieu",
        "family": "Courbariaux"
      },
      {
        "given": "Daniel",
        "family": "Soudry"
      },
      {
        "given": "Ran",
        "family": "El-Yaniv"
      },
      {
        "given": "Yoshua",
        "family": "Bengio"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We introduce a method to train Quantized Neural Networks (QNNs) --- neural networks with extremely low precision (e.g., 1-bit) weights and activations, at run-time. At train-time the quantized weights and activations are used for computing the parameter gradients. During the forward pass, QNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations. As a result, power consumption is expected to be drastically reduced. We trained QNNs over the MNIST, CIFAR-10, SVHN and ImageNet datasets. The resulting QNNs achieve prediction accuracy comparable to their 32-bit counterparts. For example, our quantized version of AlexNet with 1-bit weights and 2-bit activations achieves $51\\%$ top-1 accuracy. Moreover, we quantize the parameter gradients to 6-bits as well which enables gradients computation using only bit-wise operation. Quantized recurrent neural networks were tested over the Penn Treebank dataset, and achieved comparable accuracy as their 32-bit counterparts using only 4-bits. Last but not least, we programmed a binary matrix multiplication GPU kernel with which it is possible to run our MNIST QNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The QNN code is available online. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1609.07061"
  },
  {
    "id": "4TK06zOf",
    "URL": "https://arxiv.org/abs/1609.08144",
    "number": "1609.08144",
    "title": "Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation",
    "issued": {
      "date-parts": [
        [
          2016,
          10,
          11
        ]
      ]
    },
    "author": [
      {
        "given": "Yonghui",
        "family": "Wu"
      },
      {
        "given": "Mike",
        "family": "Schuster"
      },
      {
        "given": "Zhifeng",
        "family": "Chen"
      },
      {
        "given": "Quoc V.",
        "family": "Le"
      },
      {
        "given": "Mohammad",
        "family": "Norouzi"
      },
      {
        "given": "Wolfgang",
        "family": "Macherey"
      },
      {
        "given": "Maxim",
        "family": "Krikun"
      },
      {
        "given": "Yuan",
        "family": "Cao"
      },
      {
        "given": "Qin",
        "family": "Gao"
      },
      {
        "given": "Klaus",
        "family": "Macherey"
      },
      {
        "given": "Jeff",
        "family": "Klingner"
      },
      {
        "given": "Apurva",
        "family": "Shah"
      },
      {
        "given": "Melvin",
        "family": "Johnson"
      },
      {
        "given": "Xiaobing",
        "family": "Liu"
      },
      {
        "given": "Łukasz",
        "family": "Kaiser"
      },
      {
        "given": "Stephan",
        "family": "Gouws"
      },
      {
        "given": "Yoshikiyo",
        "family": "Kato"
      },
      {
        "given": "Taku",
        "family": "Kudo"
      },
      {
        "given": "Hideto",
        "family": "Kazawa"
      },
      {
        "given": "Keith",
        "family": "Stevens"
      },
      {
        "given": "George",
        "family": "Kurian"
      },
      {
        "given": "Nishant",
        "family": "Patil"
      },
      {
        "given": "Wei",
        "family": "Wang"
      },
      {
        "given": "Cliff",
        "family": "Young"
      },
      {
        "given": "Jason",
        "family": "Smith"
      },
      {
        "given": "Jason",
        "family": "Riesa"
      },
      {
        "given": "Alex",
        "family": "Rudnick"
      },
      {
        "given": "Oriol",
        "family": "Vinyals"
      },
      {
        "given": "Greg",
        "family": "Corrado"
      },
      {
        "given": "Macduff",
        "family": "Hughes"
      },
      {
        "given": "Jeffrey",
        "family": "Dean"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference. Also, most NMT systems have difficulty with rare words. These issues have hindered NMT's use in practical deployments and services, where both accuracy and speed are essential. In this work, we present GNMT, Google's Neural Machine Translation system, which attempts to address many of these issues. Our model consists of a deep LSTM network with 8 encoder and 8 decoder layers using attention and residual connections. To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder. To accelerate the final translation speed, we employ low-precision arithmetic during inference computations. To improve handling of rare words, we divide words into a limited set of common sub-word units (\"wordpieces\") for both input and output. This method provides a good balance between the flexibility of \"character\"-delimited models and the efficiency of \"word\"-delimited models, naturally handles translation of rare words, and ultimately improves the overall accuracy of the system. Our beam search technique employs a length-normalization procedure and uses a coverage penalty, which encourages generation of an output sentence that is most likely to cover all the words in the source sentence. On the WMT'14 English-to-French and English-to-German benchmarks, GNMT achieves competitive results to state-of-the-art. Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google's phrase-based production system. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1609.08144"
  },
  {
    "id": "Tkobp7Qj",
    "URL": "https://arxiv.org/abs/1610.02136",
    "number": "1610.02136",
    "title": "A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks",
    "issued": {
      "date-parts": [
        [
          2018,
          10,
          4
        ]
      ]
    },
    "author": [
      {
        "given": "Dan",
        "family": "Hendrycks"
      },
      {
        "given": "Kevin",
        "family": "Gimpel"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We consider the two related problems of detecting if an example is misclassified or out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maximum softmax probabilities than erroneously classified and out-of-distribution examples, allowing for their detection. We assess performance by defining several tasks in computer vision, natural language processing, and automatic speech recognition, showing the effectiveness of this baseline across all. We then show the baseline can sometimes be surpassed, demonstrating the room for future research on these underexplored detection tasks. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1610.02136"
  },
  {
    "id": "VMkPJjVk",
    "URL": "https://arxiv.org/abs/1610.02357",
    "number": "1610.02357",
    "title": "Xception: Deep Learning with Depthwise Separable Convolutions",
    "issued": {
      "date-parts": [
        [
          2017,
          4,
          6
        ]
      ]
    },
    "author": [
      {
        "given": "François",
        "family": "Chollet"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers. This observation leads us to propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions. We show that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset (which Inception V3 was designed for), and significantly outperforms Inception V3 on a larger image classification dataset comprising 350 million images and 17,000 classes. Since the Xception architecture has the same number of parameters as Inception V3, the performance gains are not due to increased capacity but rather to a more efficient use of model parameters. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1610.02357"
  },
  {
    "id": "RZsNSRDS",
    "URL": "https://arxiv.org/abs/1610.02391",
    "number": "1610.02391",
    "title": "Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization",
    "issued": {
      "date-parts": [
        [
          2019,
          12,
          4
        ]
      ]
    },
    "author": [
      {
        "given": "Ramprasaath R.",
        "family": "Selvaraju"
      },
      {
        "given": "Michael",
        "family": "Cogswell"
      },
      {
        "given": "Abhishek",
        "family": "Das"
      },
      {
        "given": "Ramakrishna",
        "family": "Vedantam"
      },
      {
        "given": "Devi",
        "family": "Parikh"
      },
      {
        "given": "Dhruv",
        "family": "Batra"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We propose a technique for producing \"visual explanations\" for decisions from a large class of CNN-based models, making them more transparent. Our approach - Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept, flowing into the final convolutional layer to produce a coarse localization map highlighting important regions in the image for predicting the concept. Grad-CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers, (2) CNNs used for structured outputs, (3) CNNs used in tasks with multimodal inputs or reinforcement learning, without any architectural changes or re-training. We combine Grad-CAM with fine-grained visualizations to create a high-resolution class-discriminative visualization and apply it to off-the-shelf image classification, captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into their failure modes, (b) are robust to adversarial images, (c) outperform previous methods on localization, (d) are more faithful to the underlying model and (e) help achieve generalization by identifying dataset bias. For captioning and VQA, we show that even non-attention based models can localize inputs. We devise a way to identify important neurons through Grad-CAM and combine it with neuron names to provide textual explanations for model decisions. Finally, we design and conduct human studies to measure if Grad-CAM helps users establish appropriate trust in predictions from models and show that Grad-CAM helps untrained users successfully discern a 'stronger' nodel from a 'weaker' one even when both make identical predictions. Our code is available at https://github.com/ramprs/grad-cam/, along with a demo at http://gradcam.cloudcv.org, and a video at youtu.be/COjUB9Izk6E. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1610.02391",
    "DOI": "10.1007/s11263-019-01228-7"
  },
  {
    "id": "1ENxzq6pT",
    "URL": "https://arxiv.org/abs/1610.02413",
    "number": "1610.02413",
    "title": "Equality of Opportunity in Supervised Learning",
    "issued": {
      "date-parts": [
        [
          2016,
          10,
          11
        ]
      ]
    },
    "author": [
      {
        "given": "Moritz",
        "family": "Hardt"
      },
      {
        "given": "Eric",
        "family": "Price"
      },
      {
        "given": "Nathan",
        "family": "Srebro"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the predictor, target, and membership in the protected group are available, we show how to optimally adjust any learned predictor so as to remove discrimination according to our definition. Our framework also improves incentives by shifting the cost of poor classification from disadvantaged groups to the decision maker, who can respond by improving the classification accuracy.\n  In line with other studies, our notion is oblivious: it depends only on the joint statistics of the predictor, the target and the protected attribute, but not on interpretation of individualfeatures. We study the inherent limits of defining and identifying biases based on such oblivious measures, outlining what can and cannot be inferred from different oblivious tests.\n  We illustrate our notion using a case study of FICO credit scores. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1610.02413"
  },
  {
    "id": "qpmV0H2p",
    "URL": "https://arxiv.org/abs/1610.02415v1",
    "number": "1610.02415v1",
    "version": "v1",
    "title": "Automatic chemical design using a data-driven continuous representation\n  of molecules",
    "issued": {
      "date-parts": [
        [
          2016,
          10,
          7
        ]
      ]
    },
    "author": [
      {
        "literal": "Rafael Gómez-Bombarelli"
      },
      {
        "literal": "David Duvenaud"
      },
      {
        "literal": "José Miguel Hernández-Lobato"
      },
      {
        "literal": "Jorge Aguilera-Iparraguirre"
      },
      {
        "literal": "Timothy D. Hirzel"
      },
      {
        "literal": "Ryan P. Adams"
      },
      {
        "literal": "Alán Aspuru-Guzik"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This generative model allows efficient search and optimization through open-ended spaces of chemical compounds. We train deep neural networks on hundreds of thousands of existing chemical structures to construct two coupled functions: an encoder and a decoder. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to the discrete representation from this latent space. Continuous representations allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the design of drug-like molecules as well as organic light-emitting diodes.",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1610.02415v1"
  },
  {
    "id": "sLPsrfbl",
    "URL": "https://arxiv.org/abs/1610.04662",
    "number": "1610.04662",
    "title": "Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images",
    "issued": {
      "date-parts": [
        [
          2016,
          10,
          19
        ]
      ]
    },
    "author": [
      {
        "given": "Noel",
        "family": "Codella"
      },
      {
        "given": "Quoc-Bao",
        "family": "Nguyen"
      },
      {
        "given": "Sharath",
        "family": "Pankanti"
      },
      {
        "given": "David",
        "family": "Gutman"
      },
      {
        "given": "Brian",
        "family": "Helba"
      },
      {
        "given": "Allan",
        "family": "Halpern"
      },
      {
        "given": "John R.",
        "family": "Smith"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Melanoma is the deadliest form of skin cancer. While curable with early detection, only highly trained specialists are capable of accurately recognizing the disease. As expertise is in limited supply, automated systems capable of identifying disease could save lives, reduce unnecessary biopsies, and reduce costs. Toward this goal, we propose a system that combines recent developments in deep learning with established machine learning approaches, creating ensembles of methods that are capable of segmenting skin lesions, as well as analyzing the detected area and surrounding tissue for melanoma detection. The system is evaluated using the largest publicly available benchmark dataset of dermoscopic images, containing 900 training and 379 testing images. New state-of-the-art performance levels are demonstrated, leading to an improvement in the area under receiver operating characteristic curve of 7.5% (0.843 vs. 0.783), in average precision of 4% (0.649 vs. 0.624), and in specificity measured at the clinically relevant 95% sensitivity operating point 2.9 times higher than the previous state-of-the-art (36.8% specificity compared to 12.5%). Compared to the average of 8 expert dermatologists on a subset of 100 test images, the proposed system produces a higher accuracy (76% vs. 70.5%), and specificity (62% vs. 59%) evaluated at an equivalent sensitivity (82%). ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1610.04662"
  },
  {
    "id": "M2OLWojE",
    "URL": "https://arxiv.org/abs/1610.05256",
    "number": "1610.05256",
    "title": "Achieving Human Parity in Conversational Speech Recognition",
    "issued": {
      "date-parts": [
        [
          2018,
          12,
          6
        ]
      ]
    },
    "author": [
      {
        "given": "W.",
        "family": "Xiong"
      },
      {
        "given": "J.",
        "family": "Droppo"
      },
      {
        "given": "X.",
        "family": "Huang"
      },
      {
        "given": "F.",
        "family": "Seide"
      },
      {
        "given": "M.",
        "family": "Seltzer"
      },
      {
        "given": "A.",
        "family": "Stolcke"
      },
      {
        "given": "D.",
        "family": "Yu"
      },
      {
        "given": "G.",
        "family": "Zweig"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Conversational speech recognition has served as a flagship speech recognition task since the release of the Switchboard corpus in the 1990s. In this paper, we measure the human error rate on the widely used NIST 2000 test set, and find that our latest automated system has reached human parity. The error rate of professional transcribers is 5.9% for the Switchboard portion of the data, in which newly acquainted pairs of people discuss an assigned topic, and 11.3% for the CallHome portion where friends and family members have open-ended conversations. In both cases, our automated system establishes a new state of the art, and edges past the human benchmark, achieving error rates of 5.8% and 11.0%, respectively. The key to our system's performance is the use of various convolutional and LSTM acoustic model architectures, combined with a novel spatial smoothing method and lattice-free MMI acoustic training, multiple recurrent neural network language modeling approaches, and a systematic use of system combination. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1610.05256"
  },
  {
    "id": "1HbRTExaU",
    "URL": "https://arxiv.org/abs/1610.05820",
    "number": "1610.05820",
    "title": "Membership Inference Attacks against Machine Learning Models",
    "issued": {
      "date-parts": [
        [
          2017,
          4,
          4
        ]
      ]
    },
    "author": [
      {
        "given": "Reza",
        "family": "Shokri"
      },
      {
        "given": "Marco",
        "family": "Stronati"
      },
      {
        "given": "Congzheng",
        "family": "Song"
      },
      {
        "given": "Vitaly",
        "family": "Shmatikov"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on.\n  We empirically evaluate our inference techniques on classification models trained by commercial \"machine learning as a service\" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1610.05820"
  },
  {
    "id": "11aqfNfQx",
    "URL": "https://arxiv.org/abs/1610.09559",
    "number": "1610.09559",
    "title": "Fair Algorithms for Infinite and Contextual Bandits",
    "issued": {
      "date-parts": [
        [
          2017,
          6,
          30
        ]
      ]
    },
    "author": [
      {
        "given": "Matthew",
        "family": "Joseph"
      },
      {
        "given": "Michael",
        "family": "Kearns"
      },
      {
        "given": "Jamie",
        "family": "Morgenstern"
      },
      {
        "given": "Seth",
        "family": "Neel"
      },
      {
        "given": "Aaron",
        "family": "Roth"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We study fairness in linear bandit problems. Starting from the notion of meritocratic fairness introduced in Joseph et al. [2016], we carry out a more refined analysis of a more general problem, achieving better performance guarantees with fewer modelling assumptions on the number and structure of available choices as well as the number selected. We also analyze the previously-unstudied question of fairness in infinite linear bandit problems, obtaining instance-dependent regret upper bounds as well as lower bounds demonstrating that this instance-dependence is necessary. The result is a framework for meritocratic fairness in an online linear setting that is substantially more powerful, general, and realistic than the current state of the art. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1610.09559"
  },
  {
    "id": "ZhgOMnnD",
    "URL": "https://arxiv.org/abs/1611.01639",
    "number": "1611.01639",
    "title": "Robustly representing uncertainty in deep neural networks through sampling",
    "issued": {
      "date-parts": [
        [
          2019,
          9,
          19
        ]
      ]
    },
    "author": [
      {
        "given": "Patrick",
        "family": "McClure"
      },
      {
        "given": "Nikolaus",
        "family": "Kriegeskorte"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  As deep neural networks (DNNs) are applied to increasingly challenging problems, they will need to be able to represent their own uncertainty. Modeling uncertainty is one of the key features of Bayesian methods. Using Bernoulli dropout with sampling at prediction time has recently been proposed as an efficient and well performing variational inference method for DNNs. However, sampling from other multiplicative noise based variational distributions has not been investigated in depth. We evaluated Bayesian DNNs trained with Bernoulli or Gaussian multiplicative masking of either the units (dropout) or the weights (dropconnect). We tested the calibration of the probabilistic predictions of Bayesian convolutional neural networks (CNNs) on MNIST and CIFAR-10. Sampling at prediction time increased the calibration of the DNNs' probabalistic predictions. Sampling weights, whether Gaussian or Bernoulli, led to more robust representation of uncertainty compared to sampling of units. However, using either Gaussian or Bernoulli dropout led to increased test set classification accuracy. Based on these findings we used both Bernoulli dropout and Gaussian dropconnect concurrently, which we show approximates the use of a spike-and-slab variational distribution without increasing the number of learned parameters. We found that spike-and-slab sampling had higher test set performance than Gaussian dropconnect and more robustly represented its uncertainty compared to Bernoulli dropout. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1611.01639"
  },
  {
    "id": "lERqKdZJ",
    "URL": "https://arxiv.org/abs/1611.02796",
    "number": "1611.02796",
    "title": "Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control",
    "issued": {
      "date-parts": [
        [
          2017,
          10,
          18
        ]
      ]
    },
    "author": [
      {
        "given": "Natasha",
        "family": "Jaques"
      },
      {
        "given": "Shixiang",
        "family": "Gu"
      },
      {
        "given": "Dzmitry",
        "family": "Bahdanau"
      },
      {
        "given": "José Miguel",
        "family": "Hernández-Lobato"
      },
      {
        "given": "Richard E.",
        "family": "Turner"
      },
      {
        "given": "Douglas",
        "family": "Eck"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  This paper proposes a general method for improving the structure and quality of sequences generated by a recurrent neural network (RNN), while maintaining information originally learned from data, as well as sample diversity. An RNN is first pre-trained on data using maximum likelihood estimation (MLE), and the probability distribution over the next token in the sequence learned by this model is treated as a prior policy. Another RNN is then trained using reinforcement learning (RL) to generate higher-quality outputs that account for domain-specific incentives while retaining proximity to the prior policy of the MLE RNN. To formalize this objective, we derive novel off-policy RL methods for RNNs from KL-control. The effectiveness of the approach is demonstrated on two applications; 1) generating novel musical melodies, and 2) computational molecular generation. For both problems, we show that the proposed method improves the desired properties and structure of the generated sequences, while maintaining information learned from data. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1611.02796"
  },
  {
    "id": "D2B03NVK",
    "URL": "https://arxiv.org/abs/1611.03530v2",
    "number": "1611.03530v2",
    "version": "v2",
    "title": "Understanding deep learning requires rethinking generalization",
    "issued": {
      "date-parts": [
        [
          2016,
          11,
          10
        ]
      ]
    },
    "author": [
      {
        "literal": "Chiyuan Zhang"
      },
      {
        "literal": "Samy Bengio"
      },
      {
        "literal": "Moritz Hardt"
      },
      {
        "literal": "Benjamin Recht"
      },
      {
        "literal": "Oriol Vinyals"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family, or to the regularization techniques used during training.\n  Through extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization, and occurs even if we replace the true images by completely unstructured random noise. We corroborate these experimental findings with a theoretical construction showing that simple depth two neural networks already have perfect finite sample expressivity as soon as the number of parameters exceeds the number of data points as it usually does in practice.\n  We interpret our experimental findings by comparison with traditional models.",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1611.03530v2"
  },
  {
    "id": "AsLAb71x",
    "URL": "https://arxiv.org/abs/1611.03814",
    "number": "1611.03814",
    "title": "Towards the Science of Security and Privacy in Machine Learning",
    "issued": {
      "date-parts": [
        [
          2016,
          11,
          14
        ]
      ]
    },
    "author": [
      {
        "given": "Nicolas",
        "family": "Papernot"
      },
      {
        "given": "Patrick",
        "family": "McDaniel"
      },
      {
        "given": "Arunesh",
        "family": "Sinha"
      },
      {
        "given": "Michael",
        "family": "Wellman"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. ML is now pervasive---new systems and models are being deployed in every domain imaginable, leading to rapid and widespread deployment of software based inference and decision making. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited. We systematize recent findings on ML security and privacy, focusing on attacks identified on these systems and defenses crafted to date. We articulate a comprehensive threat model for ML, and categorize attacks and defenses within an adversarial framework. Key insights resulting from works both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. We conclude by formally exploring the opposing relationship between model accuracy and resilience to adversarial manipulation. Through these explorations, we show that there are (possibly unavoidable) tensions between model complexity, accuracy, and resilience that must be calibrated for the environments in which they will be used. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1611.03814"
  },
  {
    "id": "10nDTiETi",
    "URL": "https://arxiv.org/abs/1611.07012",
    "number": "1611.07012",
    "title": "GRAM: Graph-based Attention Model for Healthcare Representation Learning",
    "issued": {
      "date-parts": [
        [
          2017,
          4,
          4
        ]
      ]
    },
    "author": [
      {
        "given": "Edward",
        "family": "Choi"
      },
      {
        "given": "Mohammad Taha",
        "family": "Bahadori"
      },
      {
        "given": "Le",
        "family": "Song"
      },
      {
        "given": "Walter F.",
        "family": "Stewart"
      },
      {
        "given": "Jimeng",
        "family": "Sun"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Deep learning methods exhibit promising performance for predictive modeling in healthcare, but two important challenges remain: -Data insufficiency:Often in healthcare predictive modeling, the sample size is insufficient for deep learning methods to achieve satisfactory results. -Interpretation:The representations learned by deep learning methods should align with medical knowledge. To address these challenges, we propose a GRaph-based Attention Model, GRAM that supplements electronic health records (EHR) with hierarchical information inherent to medical ontologies. Based on the data volume and the ontology structure, GRAM represents a medical concept as a combination of its ancestors in the ontology via an attention mechanism. We compared predictive performance (i.e. accuracy, data needs, interpretability) of GRAM to various methods including the recurrent neural network (RNN) in two sequential diagnoses prediction tasks and one heart failure prediction task. Compared to the basic RNN, GRAM achieved 10% higher accuracy for predicting diseases rarely observed in the training data and 3% improved area under the ROC curve for predicting heart failure using an order of magnitude less training data. Additionally, unlike other methods, the medical concept representations learned by GRAM are well aligned with the medical ontology. Finally, GRAM exhibits intuitive attention behaviors by adaptively generalizing to higher level concepts when facing data insufficiency at the lower level concepts. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1611.07012"
  },
  {
    "id": "b1sc0cgP",
    "URL": "https://arxiv.org/abs/1611.07270",
    "number": "1611.07270",
    "title": "Investigating the influence of noise and distractors on the interpretation of neural networks",
    "issued": {
      "date-parts": [
        [
          2016,
          11,
          23
        ]
      ]
    },
    "author": [
      {
        "given": "Pieter-Jan",
        "family": "Kindermans"
      },
      {
        "given": "Kristof",
        "family": "Schütt"
      },
      {
        "given": "Klaus-Robert",
        "family": "Müller"
      },
      {
        "given": "Sven",
        "family": "Dähne"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Understanding neural networks is becoming increasingly important. Over the last few years different types of visualisation and explanation methods have been proposed. However, none of them explicitly considered the behaviour in the presence of noise and distracting elements. In this work, we will show how noise and distracting dimensions can influence the result of an explanation model. This gives a new theoretical insights to aid selection of the most appropriate explanation model within the deep-Taylor decomposition framework. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1611.07270"
  },
  {
    "id": "DeOI1oGf",
    "URL": "https://arxiv.org/abs/1611.07478",
    "number": "1611.07478",
    "title": "An unexpected unity among methods for interpreting model predictions",
    "issued": {
      "date-parts": [
        [
          2016,
          12,
          9
        ]
      ]
    },
    "author": [
      {
        "given": "Scott",
        "family": "Lundberg"
      },
      {
        "given": "Su-In",
        "family": "Lee"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Understanding why a model made a certain prediction is crucial in many data science fields. Interpretable predictions engender appropriate trust and provide insight into how the model may be improved. However, with large modern datasets the best accuracy is often achieved by complex models even experts struggle to interpret, which creates a tension between accuracy and interpretability. Recently, several methods have been proposed for interpreting predictions from complex models by estimating the importance of input features. Here, we present how a model-agnostic additive representation of the importance of input features unifies current methods. This representation is optimal, in the sense that it is the only set of additive values that satisfies important properties. We show how we can leverage these properties to create novel visual explanations of model predictions. The thread of unity that this representation weaves through the literature indicates that there are common principles to be learned about the interpretation of model predictions that apply in many scenarios. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1611.07478"
  },
  {
    "id": "dO844vZn",
    "URL": "https://arxiv.org/abs/1611.08373",
    "number": "1611.08373",
    "title": "Bidirectional LSTM-CRF for Clinical Concept Extraction",
    "issued": {
      "date-parts": [
        [
          2016,
          11,
          28
        ]
      ]
    },
    "author": [
      {
        "given": "Raghavendra",
        "family": "Chalapathy"
      },
      {
        "given": "Ehsan Zare",
        "family": "Borzeshi"
      },
      {
        "given": "Massimo",
        "family": "Piccardi"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for Clinical Records introduced a concept extraction task aimed at identifying and classifying concepts into predefined categories (i.e., treatments, tests and problems). State-of-the-art concept extraction approaches heavily rely on handcrafted features and domain-specific resources which are hard to collect and define. For this reason, this paper proposes an alternative, streamlined approach: a recurrent neural network (the bidirectional LSTM with CRF decoding) initialized with general-purpose, off-the-shelf word embeddings. The experimental results achieved on the 2010 i2b2/VA reference corpora using the proposed framework outperform all recent methods and ranks closely to the best submission from the original 2010 i2b2/VA challenge. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1611.08373"
  },
  {
    "id": "Jy2oz6xk",
    "URL": "https://arxiv.org/abs/1612.01474",
    "number": "1612.01474",
    "title": "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles",
    "issued": {
      "date-parts": [
        [
          2017,
          11,
          7
        ]
      ]
    },
    "author": [
      {
        "given": "Balaji",
        "family": "Lakshminarayanan"
      },
      {
        "given": "Alexander",
        "family": "Pritzel"
      },
      {
        "given": "Charles",
        "family": "Blundell"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem. Bayesian NNs, which learn a distribution over weights, are currently the state-of-the-art for estimating predictive uncertainty; however these require significant modifications to the training procedure and are computationally expensive compared to standard (non-Bayesian) NNs. We propose an alternative to Bayesian NNs that is simple to implement, readily parallelizable, requires very little hyperparameter tuning, and yields high quality predictive uncertainty estimates. Through a series of experiments on classification and regression benchmarks, we demonstrate that our method produces well-calibrated uncertainty estimates which are as good or better than approximate Bayesian NNs. To assess robustness to dataset shift, we evaluate the predictive uncertainty on test examples from known and unknown distributions, and show that our method is able to express higher uncertainty on out-of-distribution examples. We demonstrate the scalability of our method by evaluating predictive uncertainty estimates on ImageNet. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1612.01474"
  },
  {
    "id": "bNBiIiTt",
    "URL": "https://arxiv.org/abs/1612.02751",
    "number": "1612.02751",
    "title": "Protein-Ligand Scoring with Convolutional Neural Networks",
    "issued": {
      "date-parts": [
        [
          2016,
          12,
          9
        ]
      ]
    },
    "author": [
      {
        "given": "Matthew",
        "family": "Ragoza"
      },
      {
        "given": "Joshua",
        "family": "Hochuli"
      },
      {
        "given": "Elisa",
        "family": "Idrobo"
      },
      {
        "given": "Jocelyn",
        "family": "Sunseri"
      },
      {
        "given": "David Ryan",
        "family": "Koes"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding and structural data enables the use of deep machine learning techniques for protein-ligand scoring.\n  We describe convolutional neural network (CNN) scoring functions that take as input a comprehensive 3D representation of a protein-ligand interaction. A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding. We train and optimize our CNN scoring functions to discriminate between correct and incorrect binding poses and known binders and non-binders. We find that our CNN scoring function outperforms the AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1612.02751"
  },
  {
    "id": "8LWFFeYg",
    "URL": "https://arxiv.org/abs/1701.01329",
    "number": "1701.01329",
    "title": "Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks",
    "issued": {
      "date-parts": [
        [
          2017,
          1,
          6
        ]
      ]
    },
    "author": [
      {
        "given": "Marwin H. S.",
        "family": "Segler"
      },
      {
        "given": "Thierry",
        "family": "Kogej"
      },
      {
        "given": "Christian",
        "family": "Tyrchan"
      },
      {
        "given": "Mark P.",
        "family": "Waller"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active towards a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target.\n  Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria) it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1701.01329"
  },
  {
    "id": "10ViHstXn",
    "URL": "https://arxiv.org/abs/1702.02540",
    "number": "1702.02540",
    "title": "Automatic Rule Extraction from Long Short Term Memory Networks",
    "issued": {
      "date-parts": [
        [
          2017,
          2,
          28
        ]
      ]
    },
    "author": [
      {
        "given": "W. James",
        "family": "Murdoch"
      },
      {
        "given": "Arthur",
        "family": "Szlam"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Although deep learning models have proven effective at solving problems in natural language processing, the mechanism by which they come to their conclusions is often unclear. As a result, these models are generally treated as black boxes, yielding no insight of the underlying learned patterns. In this paper we consider Long Short Term Memory networks (LSTMs) and demonstrate a new approach for tracking the importance of a given input to the LSTM for a given output. By identifying consistently important patterns of words, we are able to distill state of the art LSTMs on sentiment analysis and question answering into a set of representative phrases. This representation is then quantitatively validated by using the extracted phrases to construct a simple, rule-based classifier which approximates the output of the LSTM. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1702.02540"
  },
  {
    "id": "Kk20paR7",
    "URL": "https://arxiv.org/abs/1702.04595",
    "number": "1702.04595",
    "title": "Visualizing Deep Neural Network Decisions: Prediction Difference Analysis",
    "issued": {
      "date-parts": [
        [
          2017,
          2,
          16
        ]
      ]
    },
    "author": [
      {
        "given": "Luisa M",
        "family": "Zintgraf"
      },
      {
        "given": "Taco S",
        "family": "Cohen"
      },
      {
        "given": "Tameem",
        "family": "Adel"
      },
      {
        "given": "Max",
        "family": "Welling"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input. When classifying images, the method highlights areas in a given input image that provide evidence for or against a certain class. It overcomes several shortcoming of previous methods and provides great additional insight into the decision making process of classifiers. Making neural network decisions interpretable through visualization is important both to improve models and to accelerate the adoption of black-box classifiers in application areas such as medicine. We illustrate the method in experiments on natural images (ImageNet data), as well as medical images (MRI brain scans). ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1702.04595"
  },
  {
    "id": "WzFOJBiA",
    "URL": "https://arxiv.org/abs/1703.01365",
    "number": "1703.01365",
    "title": "Axiomatic Attribution for Deep Networks",
    "issued": {
      "date-parts": [
        [
          2017,
          6,
          14
        ]
      ]
    },
    "author": [
      {
        "given": "Mukund",
        "family": "Sundararajan"
      },
      {
        "given": "Ankur",
        "family": "Taly"
      },
      {
        "given": "Qiqi",
        "family": "Yan"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We study the problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works. We identify two fundamental axioms---Sensitivity and Implementation Invariance that attribution methods ought to satisfy. We show that they are not satisfied by most known attribution methods, which we consider to be a fundamental weakness of those methods. We use the axioms to guide the design of a new attribution method called Integrated Gradients. Our method requires no modification to the original network and is extremely simple to implement; it just needs a few calls to the standard gradient operator. We apply this method to a couple of image models, a couple of text models and a chemistry model, demonstrating its ability to debug networks, to extract rules from a network, and to enable users to engage with models better. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1703.01365"
  },
  {
    "id": "AQ3N6Ayw",
    "URL": "https://arxiv.org/abs/1703.01925",
    "number": "1703.01925",
    "title": "Grammar Variational Autoencoder",
    "issued": {
      "date-parts": [
        [
          2017,
          3,
          7
        ]
      ]
    },
    "author": [
      {
        "given": "Matt J.",
        "family": "Kusner"
      },
      {
        "given": "Brooks",
        "family": "Paige"
      },
      {
        "given": "José Miguel",
        "family": "Hernández-Lobato"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Deep generative models have been wildly successful at learning coherent latent representations for continuous data such as video and audio. However, generative modeling of discrete data such as arithmetic expressions and molecular structures still poses significant challenges. Crucially, state-of-the-art methods often produce outputs that are not valid. We make the key observation that frequently, discrete data can be represented as a parse tree from a context-free grammar. We propose a variational autoencoder which encodes and decodes directly to and from these parse trees, ensuring the generated outputs are always valid. Surprisingly, we show that not only does our model more often generate valid outputs, it also learns a more coherent latent space in which nearby points decode to similar discrete outputs. We demonstrate the effectiveness of our learned models by showing their improved performance in Bayesian optimization for symbolic regression and molecular synthesis. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1703.01925"
  },
  {
    "id": "wKioubsT",
    "URL": "https://arxiv.org/abs/1703.02136",
    "number": "1703.02136",
    "title": "English Conversational Telephone Speech Recognition by Humans and Machines",
    "issued": {
      "date-parts": [
        [
          2017,
          3,
          8
        ]
      ]
    },
    "author": [
      {
        "given": "George",
        "family": "Saon"
      },
      {
        "given": "Gakuto",
        "family": "Kurata"
      },
      {
        "given": "Tom",
        "family": "Sercu"
      },
      {
        "given": "Kartik",
        "family": "Audhkhasi"
      },
      {
        "given": "Samuel",
        "family": "Thomas"
      },
      {
        "given": "Dimitrios",
        "family": "Dimitriadis"
      },
      {
        "given": "Xiaodong",
        "family": "Cui"
      },
      {
        "given": "Bhuvana",
        "family": "Ramabhadran"
      },
      {
        "given": "Michael",
        "family": "Picheny"
      },
      {
        "given": "Lynn-Li",
        "family": "Lim"
      },
      {
        "given": "Bergul",
        "family": "Roomi"
      },
      {
        "given": "Phil",
        "family": "Hall"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  One of the most difficult speech recognition tasks is accurate recognition of human to human communication. Advances in deep learning over the last few years have produced major speech recognition improvements on the representative Switchboard conversational corpus. Word error rates that just a few years ago were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now believed to be within striking range of human performance. This then raises two issues - what IS human performance, and how far down can we still drive speech recognition error rates? A recent paper by Microsoft suggests that we have already achieved human performance. In trying to verify this statement, we performed an independent set of human performance measurements on two conversational tasks and found that human performance may be considerably better than what was earlier reported, giving the community a significantly harder goal to achieve. We also report on our own efforts in this area, presenting a set of acoustic and language modeling techniques that lowered the word error rate of our own English conversational telephone LVCSR system to the level of 5.5%/10.3% on the Switchboard/CallHome subsets of the Hub5 2000 evaluation, which - at least at the writing of this paper - is a new performance milestone (albeit not at what we measure to be human performance!). On the acoustic side, we use a score fusion of three models: one LSTM with multiple feature inputs, a second LSTM trained with speaker-adversarial multi-task learning and a third residual net (ResNet) with 25 convolutional layers and time-dilated convolutions. On the language modeling side, we use word and character LSTMs and convolutional WaveNet-style language models. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1703.02136"
  },
  {
    "id": "69wxD9y",
    "URL": "https://arxiv.org/abs/1703.04730",
    "number": "1703.04730",
    "title": "Understanding Black-box Predictions via Influence Functions",
    "issued": {
      "date-parts": [
        [
          2017,
          7,
          11
        ]
      ]
    },
    "author": [
      {
        "given": "Pang Wei",
        "family": "Koh"
      },
      {
        "given": "Percy",
        "family": "Liang"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  How can we explain the predictions of a black-box model? In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only oracle access to gradients and Hessian-vector products. We show that even on non-convex and non-differentiable models where the theory breaks down, approximations to influence functions can still provide valuable information. On linear models and convolutional neural networks, we demonstrate that influence functions are useful for multiple purposes: understanding model behavior, debugging models, detecting dataset errors, and even creating visually-indistinguishable training-set attacks. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1703.04730"
  },
  {
    "id": "1GDnudDWl",
    "URL": "https://arxiv.org/abs/1703.04977",
    "number": "1703.04977",
    "title": "What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?",
    "issued": {
      "date-parts": [
        [
          2017,
          10,
          6
        ]
      ]
    },
    "author": [
      {
        "given": "Alex",
        "family": "Kendall"
      },
      {
        "given": "Yarin",
        "family": "Gal"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for uncertainty in the model -- uncertainty which can be explained away given enough data. Traditionally it has been difficult to model epistemic uncertainty in computer vision, but with new Bayesian deep learning tools this is now possible. We study the benefits of modeling epistemic vs. aleatoric uncertainty in Bayesian deep learning models for vision tasks. For this we present a Bayesian deep learning framework combining input-dependent aleatoric uncertainty together with epistemic uncertainty. We study models under the framework with per-pixel semantic segmentation and depth regression tasks. Further, our explicit uncertainty formulation leads to new loss functions for these tasks, which can be interpreted as learned attenuation. This makes the loss more robust to noisy data, also giving new state-of-the-art results on segmentation and depth regression benchmarks. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1703.04977"
  },
  {
    "id": "xl1ijigK",
    "URL": "https://arxiv.org/abs/1703.06490v1",
    "number": "1703.06490v1",
    "version": "v1",
    "title": "Generating Multi-label Discrete Electronic Health Records using\n  Generative Adversarial Networks",
    "issued": {
      "date-parts": [
        [
          2017,
          3,
          19
        ]
      ]
    },
    "author": [
      {
        "literal": "Edward Choi"
      },
      {
        "literal": "Siddharth Biswal"
      },
      {
        "literal": "Bradley Malin"
      },
      {
        "literal": "Jon Duke"
      },
      {
        "literal": "Walter F. Stewart"
      },
      {
        "literal": "Jimeng Sun"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "Access to electronic health records (EHR) data has motivated computational advances in medical research. However, various concerns, particularly over privacy, can limit access to and collaborative use of EHR data. Sharing synthetic EHR data could mitigate risk. In this paper, we propose a new approach, medical Generative Adversarial Network (medGAN), to generate realistic synthetic EHRs. Based on an input EHR dataset, medGAN can generate high-dimensional discrete variables (e.g., binary and count features) via a combination of an autoencoder and generative adversarial networks. We also propose minibatch averaging to efficiently avoid mode collapse, and increase the learning efficiency with batch normalization and shortcut connections. To demonstrate feasibility, we showed that medGAN generates synthetic EHR datasets that achieve comparable performance to real data on many experiments including distribution statistics, predictive modeling tasks and medical expert review.",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1703.06490v1"
  },
  {
    "id": "17YaKNLKk",
    "URL": "https://arxiv.org/abs/1703.10603",
    "number": "1703.10603",
    "title": "Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity",
    "issued": {
      "date-parts": [
        [
          2017,
          3,
          31
        ]
      ]
    },
    "author": [
      {
        "given": "Joseph",
        "family": "Gomes"
      },
      {
        "given": "Bharath",
        "family": "Ramsundar"
      },
      {
        "given": "Evan N.",
        "family": "Feinberg"
      },
      {
        "given": "Vijay S.",
        "family": "Pande"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Empirical scoring functions based on either molecular force fields or cheminformatics descriptors are widely used, in conjunction with molecular docking, during the early stages of drug discovery to predict potency and binding affinity of a drug-like molecule to a given target. These models require expert-level knowledge of physical chemistry and biology to be encoded as hand-tuned parameters or features rather than allowing the underlying model to select features in a data-driven procedure. Here, we develop a general 3-dimensional spatial convolution operation for learning atomic-level chemical interactions directly from atomic coordinates and demonstrate its application to structure-based bioactivity prediction. The atomic convolutional neural network is trained to predict the experimentally determined binding affinity of a protein-ligand complex by direct calculation of the energy associated with the complex, protein, and ligand given the crystal structure of the binding pose. Non-covalent interactions present in the complex that are absent in the protein-ligand sub-structures are identified and the model learns the interaction strength associated with these features. We test our model by predicting the binding free energy of a subset of protein-ligand complexes found in the PDBBind dataset and compare with state-of-the-art cheminformatics and machine learning-based approaches. We find that all methods achieve experimental accuracy and that atomic convolutional networks either outperform or perform competitively with the cheminformatics based methods. Unlike all previous protein-ligand prediction systems, atomic convolutional networks are end-to-end and fully-differentiable. They represent a new data-driven, physics-based deep learning model paradigm that offers a strong foundation for future improvements in structure-based bioactivity prediction. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1703.10603"
  },
  {
    "id": "18lZK7fxH",
    "URL": "https://arxiv.org/abs/1704.01155",
    "number": "1704.01155",
    "title": "Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks",
    "issued": {
      "date-parts": [
        [
          2017,
          12,
          7
        ]
      ]
    },
    "author": [
      {
        "given": "Weilin",
        "family": "Xu"
      },
      {
        "given": "David",
        "family": "Evans"
      },
      {
        "given": "Yanjun",
        "family": "Qi"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Although deep neural networks (DNNs) have achieved great success in many tasks, they can often be fooled by \\emph{adversarial examples} that are generated by adding small but purposeful distortions to natural examples. Previous studies to defend against adversarial examples mostly focused on refining the DNN models, but have either shown limited success or required expensive computation. We propose a new strategy, \\emph{feature squeezing}, that can be used to harden DNN models by detecting adversarial examples. Feature squeezing reduces the search space available to an adversary by coalescing samples that correspond to many different feature vectors in the original space into a single sample. By comparing a DNN model's prediction on the original input with that on squeezed inputs, feature squeezing detects adversarial examples with high accuracy and few false positives. This paper explores two feature squeezing methods: reducing the color bit depth of each pixel and spatial smoothing. These simple strategies are inexpensive and complementary to other defenses, and can be combined in a joint detection framework to achieve high detection rates against state-of-the-art attacks. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1704.01155",
    "DOI": "10.14722/ndss.2018.23198"
  },
  {
    "id": "QphVo2P2",
    "URL": "https://arxiv.org/abs/1704.01942",
    "number": "1704.01942",
    "title": "ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models",
    "issued": {
      "date-parts": [
        [
          2017,
          8,
          10
        ]
      ]
    },
    "author": [
      {
        "given": "Minsuk",
        "family": "Kahng"
      },
      {
        "given": "Pierre Y.",
        "family": "Andrews"
      },
      {
        "given": "Aditya",
        "family": "Kalro"
      },
      {
        "given": "Duen Horng",
        "family": "Chau"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ActiVis, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance- and subset-level. ActiVis has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ActiVis may work with different models. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1704.01942"
  },
  {
    "id": "zhmq9ktJ",
    "URL": "https://arxiv.org/abs/1704.02685",
    "number": "1704.02685",
    "title": "Learning Important Features Through Propagating Activation Differences",
    "issued": {
      "date-parts": [
        [
          2019,
          10,
          15
        ]
      ]
    },
    "author": [
      {
        "given": "Avanti",
        "family": "Shrikumar"
      },
      {
        "given": "Peyton",
        "family": "Greenside"
      },
      {
        "given": "Anshul",
        "family": "Kundaje"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  The purported \"black box\" nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods. Video tutorial: http://goo.gl/qKb7pL, ICML slides: bit.ly/deeplifticmlslides, ICML talk: https://vimeo.com/238275076, code: http://goo.gl/RM8jvH. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1704.02685"
  },
  {
    "id": "ULagTifF",
    "URL": "https://arxiv.org/abs/1704.04760",
    "number": "1704.04760",
    "title": "In-Datacenter Performance Analysis of a Tensor Processing Unit",
    "issued": {
      "date-parts": [
        [
          2017,
          4,
          18
        ]
      ]
    },
    "author": [
      {
        "given": "Norman P.",
        "family": "Jouppi"
      },
      {
        "given": "Cliff",
        "family": "Young"
      },
      {
        "given": "Nishant",
        "family": "Patil"
      },
      {
        "given": "David",
        "family": "Patterson"
      },
      {
        "given": "Gaurav",
        "family": "Agrawal"
      },
      {
        "given": "Raminder",
        "family": "Bajwa"
      },
      {
        "given": "Sarah",
        "family": "Bates"
      },
      {
        "given": "Suresh",
        "family": "Bhatia"
      },
      {
        "given": "Nan",
        "family": "Boden"
      },
      {
        "given": "Al",
        "family": "Borchers"
      },
      {
        "given": "Rick",
        "family": "Boyle"
      },
      {
        "given": "Pierre-luc",
        "family": "Cantin"
      },
      {
        "given": "Clifford",
        "family": "Chao"
      },
      {
        "given": "Chris",
        "family": "Clark"
      },
      {
        "given": "Jeremy",
        "family": "Coriell"
      },
      {
        "given": "Mike",
        "family": "Daley"
      },
      {
        "given": "Matt",
        "family": "Dau"
      },
      {
        "given": "Jeffrey",
        "family": "Dean"
      },
      {
        "given": "Ben",
        "family": "Gelb"
      },
      {
        "given": "Tara Vazir",
        "family": "Ghaemmaghami"
      },
      {
        "given": "Rajendra",
        "family": "Gottipati"
      },
      {
        "given": "William",
        "family": "Gulland"
      },
      {
        "given": "Robert",
        "family": "Hagmann"
      },
      {
        "given": "C. Richard",
        "family": "Ho"
      },
      {
        "given": "Doug",
        "family": "Hogberg"
      },
      {
        "given": "John",
        "family": "Hu"
      },
      {
        "given": "Robert",
        "family": "Hundt"
      },
      {
        "given": "Dan",
        "family": "Hurt"
      },
      {
        "given": "Julian",
        "family": "Ibarz"
      },
      {
        "given": "Aaron",
        "family": "Jaffey"
      },
      {
        "given": "Alek",
        "family": "Jaworski"
      },
      {
        "given": "Alexander",
        "family": "Kaplan"
      },
      {
        "given": "Harshit",
        "family": "Khaitan"
      },
      {
        "given": "Andy",
        "family": "Koch"
      },
      {
        "given": "Naveen",
        "family": "Kumar"
      },
      {
        "given": "Steve",
        "family": "Lacy"
      },
      {
        "given": "James",
        "family": "Laudon"
      },
      {
        "given": "James",
        "family": "Law"
      },
      {
        "given": "Diemthu",
        "family": "Le"
      },
      {
        "given": "Chris",
        "family": "Leary"
      },
      {
        "given": "Zhuyuan",
        "family": "Liu"
      },
      {
        "given": "Kyle",
        "family": "Lucke"
      },
      {
        "given": "Alan",
        "family": "Lundin"
      },
      {
        "given": "Gordon",
        "family": "MacKean"
      },
      {
        "given": "Adriana",
        "family": "Maggiore"
      },
      {
        "given": "Maire",
        "family": "Mahony"
      },
      {
        "given": "Kieran",
        "family": "Miller"
      },
      {
        "given": "Rahul",
        "family": "Nagarajan"
      },
      {
        "given": "Ravi",
        "family": "Narayanaswami"
      },
      {
        "given": "Ray",
        "family": "Ni"
      },
      {
        "given": "Kathy",
        "family": "Nix"
      },
      {
        "given": "Thomas",
        "family": "Norrie"
      },
      {
        "given": "Mark",
        "family": "Omernick"
      },
      {
        "given": "Narayana",
        "family": "Penukonda"
      },
      {
        "given": "Andy",
        "family": "Phelps"
      },
      {
        "given": "Jonathan",
        "family": "Ross"
      },
      {
        "given": "Matt",
        "family": "Ross"
      },
      {
        "given": "Amir",
        "family": "Salek"
      },
      {
        "given": "Emad",
        "family": "Samadiani"
      },
      {
        "given": "Chris",
        "family": "Severn"
      },
      {
        "given": "Gregory",
        "family": "Sizikov"
      },
      {
        "given": "Matthew",
        "family": "Snelham"
      },
      {
        "given": "Jed",
        "family": "Souter"
      },
      {
        "given": "Dan",
        "family": "Steinberg"
      },
      {
        "given": "Andy",
        "family": "Swing"
      },
      {
        "given": "Mercedes",
        "family": "Tan"
      },
      {
        "given": "Gregory",
        "family": "Thorson"
      },
      {
        "given": "Bo",
        "family": "Tian"
      },
      {
        "given": "Horia",
        "family": "Toma"
      },
      {
        "given": "Erick",
        "family": "Tuttle"
      },
      {
        "given": "Vijay",
        "family": "Vasudevan"
      },
      {
        "given": "Richard",
        "family": "Walter"
      },
      {
        "given": "Walter",
        "family": "Wang"
      },
      {
        "given": "Eric",
        "family": "Wilcox"
      },
      {
        "given": "Doe Hyun",
        "family": "Yoon"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1704.04760"
  },
  {
    "id": "39RPiE10",
    "URL": "https://arxiv.org/abs/1704.07207",
    "number": "1704.07207",
    "title": "Predicting membrane protein contacts from non-membrane proteins by deep transfer learning",
    "issued": {
      "date-parts": [
        [
          2017,
          5,
          11
        ]
      ]
    },
    "author": [
      {
        "given": "Zhen",
        "family": "Li"
      },
      {
        "given": "Sheng",
        "family": "Wang"
      },
      {
        "given": "Yizhou",
        "family": "Yu"
      },
      {
        "given": "Jinbo",
        "family": "Xu"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Computational prediction of membrane protein (MP) structures is very challenging partially due to lack of sufficient solved structures for homology modeling. Recently direct evolutionary coupling analysis (DCA) sheds some light on protein contact prediction and accordingly, contact-assisted folding, but DCA is effective only on some very large-sized families since it uses information only in a single protein family. This paper presents a deep transfer learning method that can significantly improve MP contact prediction by learning contact patterns and complex sequence-contact relationship from thousands of non-membrane proteins (non-MPs). Tested on 510 non-redundant MPs, our deep model (learned from only non-MPs) has top L/10 long-range contact prediction accuracy 0.69, better than our deep model trained by only MPs (0.63) and much better than a representative DCA method CCMpred (0.47) and the CASP11 winner MetaPSICOV (0.55). The accuracy of our deep model can be further improved to 0.72 when trained by a mix of non-MPs and MPs. When only contacts in transmembrane regions are evaluated, our method has top L/10 long-range accuracy 0.62, 0.57, and 0.53 when trained by a mix of non-MPs and MPs, by non-MPs only, and by MPs only, respectively, still much better than MetaPSICOV (0.45) and CCMpred (0.40). All these results suggest that sequence-structure relationship learned by our deep model from non-MPs generalizes well to MP contact prediction. Improved contact prediction also leads to better contact-assisted folding. Using only top predicted contacts as restraints, our deep learning method can fold 160 and 200 of 510 MPs with TMscore>0.6 when trained by non-MPs only and by a mix of non-MPs and MPs, respectively, while CCMpred and MetaPSICOV can do so for only 56 and 77 MPs, respectively. Our contact-assisted folding also greatly outperforms homology modeling. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1704.07207"
  },
  {
    "id": "1EayJRsI",
    "URL": "https://arxiv.org/abs/1704.07555",
    "number": "1704.07555",
    "title": "Molecular De Novo Design through Deep Reinforcement Learning",
    "issued": {
      "date-parts": [
        [
          2017,
          8,
          30
        ]
      ]
    },
    "author": [
      {
        "given": "Marcus",
        "family": "Olivecrona"
      },
      {
        "given": "Thomas",
        "family": "Blaschke"
      },
      {
        "given": "Ola",
        "family": "Engkvist"
      },
      {
        "given": "Hongming",
        "family": "Chen"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1704.07555"
  },
  {
    "id": "71c6rs2z",
    "URL": "https://arxiv.org/abs/1705.00092",
    "number": "1705.00092",
    "title": "Generative Modeling with Conditional Autoencoders: Building an Integrated Cell",
    "issued": {
      "date-parts": [
        [
          2017,
          5,
          2
        ]
      ]
    },
    "author": [
      {
        "given": "Gregory R.",
        "family": "Johnson"
      },
      {
        "given": "Rory M.",
        "family": "Donovan-Maiye"
      },
      {
        "given": "Mary M.",
        "family": "Maleckar"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We present a conditional generative model to learn variation in cell and nuclear morphology and the location of subcellular structures from microscopy images. Our model generalizes to a wide range of subcellular localization and allows for a probabilistic interpretation of cell and nuclear morphology and structure localization from fluorescence images. We demonstrate the effectiveness of our approach by producing photo-realistic cell images using our generative model. The conditional nature of the model provides the ability to predict the localization of unobserved structures given cell and nuclear morphology. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1705.00092"
  },
  {
    "id": "PGi9g7yV",
    "URL": "https://arxiv.org/abs/1705.02315",
    "number": "1705.02315",
    "title": "ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases",
    "issued": {
      "date-parts": [
        [
          2019,
          2,
          1
        ]
      ]
    },
    "author": [
      {
        "given": "Xiaosong",
        "family": "Wang"
      },
      {
        "given": "Yifan",
        "family": "Peng"
      },
      {
        "given": "Le",
        "family": "Lu"
      },
      {
        "given": "Zhiyong",
        "family": "Lu"
      },
      {
        "given": "Mohammadhadi",
        "family": "Bagheri"
      },
      {
        "given": "Ronald M.",
        "family": "Summers"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases. A tremendous number of X-ray imaging studies accompanied by radiological reports are accumulated and stored in many modern hospitals' Picture Archiving and Communication Systems (PACS). On the other side, it is still an open question how this type of hospital-size knowledge database containing invaluable imaging informatics (i.e., loosely labeled) can be used to facilitate the data-hungry deep learning paradigms in building truly large-scale high precision computer-aided diagnosis (CAD) systems.\n  In this paper, we present a new chest X-ray database, namely \"ChestX-ray8\", which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels (where each image can have multi-labels), from the associated radiological reports using natural language processing. Importantly, we demonstrate that these commonly occurring thoracic diseases can be detected and even spatially-located via a unified weakly-supervised multi-label image classification and disease localization framework, which is validated using our proposed dataset. Although the initial quantitative results are promising as reported, deep convolutional neural network based \"reading chest X-rays\" (i.e., recognizing and locating the common disease patterns trained with only image-level labels) remains a strenuous task for fully-automated high precision CAD systems. Data download link: https://nihcc.app.box.com/v/ChestXray-NIHCC ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1705.02315",
    "DOI": "10.1109/CVPR.2017.369"
  },
  {
    "id": "M6JCKCLX",
    "URL": "https://arxiv.org/abs/1705.03261",
    "number": "1705.03261",
    "title": "Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers",
    "issued": {
      "date-parts": [
        [
          2017,
          5,
          19
        ]
      ]
    },
    "author": [
      {
        "given": "Zibo",
        "family": "Yi"
      },
      {
        "given": "Shasha",
        "family": "Li"
      },
      {
        "given": "Jie",
        "family": "Yu"
      },
      {
        "given": "Qingbo",
        "family": "Wu"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Drug-drug interaction (DDI) is a vital information when physicians and pharmacists intend to co-administer two or more drugs. Thus, several DDI databases are constructed to avoid mistakenly combined use. In recent years, automatically extracting DDIs from biomedical text has drawn researchers' attention. However, the existing work utilize either complex feature engineering or NLP tools, both of which are insufficient for sentence comprehension. Inspired by the deep learning approaches in natural language processing, we propose a recur- rent neural network model with multiple attention layers for DDI classification. We evaluate our model on 2013 SemEval DDIExtraction dataset. The experiments show that our model classifies most of the drug pairs into correct DDI categories, which outperforms the existing NLP or deep learning methods. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1705.03261"
  },
  {
    "id": "WZjlu7tr",
    "URL": "https://arxiv.org/abs/1705.07115",
    "number": "1705.07115",
    "title": "Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics",
    "issued": {
      "date-parts": [
        [
          2018,
          4,
          25
        ]
      ]
    },
    "author": [
      {
        "given": "Alex",
        "family": "Kendall"
      },
      {
        "given": "Yarin",
        "family": "Gal"
      },
      {
        "given": "Roberto",
        "family": "Cipolla"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives. In this paper we make the observation that the performance of such systems is strongly dependent on the relative weighting between each task's loss. Tuning these weights by hand is a difficult and expensive process, making multi-task learning prohibitive in practice. We propose a principled approach to multi-task deep learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. This allows us to simultaneously learn various quantities with different units or scales in both classification and regression settings. We demonstrate our model learning per-pixel depth regression, semantic and instance segmentation from a monocular input image. Perhaps surprisingly, we show our model can learn multi-task weightings and outperform separate models trained individually on each task. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1705.07115"
  },
  {
    "id": "b5zIzCEO",
    "URL": "https://arxiv.org/abs/1705.07263",
    "number": "1705.07263",
    "title": "Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods",
    "issued": {
      "date-parts": [
        [
          2017,
          11,
          2
        ]
      ]
    },
    "author": [
      {
        "given": "Nicholas",
        "family": "Carlini"
      },
      {
        "given": "David",
        "family": "Wagner"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Neural networks are known to be vulnerable to adversarial examples: inputs that are close to natural inputs but classified incorrectly. In order to better understand the space of adversarial examples, we survey ten recent proposals that are designed for detection and compare their efficacy. We show that all can be defeated by constructing new loss functions. We conclude that adversarial examples are significantly harder to detect than previously appreciated, and the properties believed to be intrinsic to adversarial examples are in fact not. Finally, we propose several simple guidelines for evaluating future proposed defenses. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1705.07263"
  },
  {
    "id": "z8hmfrmY",
    "URL": "https://arxiv.org/abs/1705.09516",
    "number": "1705.09516",
    "title": "Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models",
    "issued": {
      "date-parts": [
        [
          2017,
          5,
          29
        ]
      ]
    },
    "author": [
      {
        "given": "Patchigolla V S S",
        "family": "Rahul"
      },
      {
        "given": "Sunil Kumar",
        "family": "Sahu"
      },
      {
        "given": "Ashish",
        "family": "Anand"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Biomedical events describe complex interactions between various biomedical entities. Event trigger is a word or a phrase which typically signifies the occurrence of an event. Event trigger identification is an important first step in all event extraction methods. However many of the current approaches either rely on complex hand-crafted features or consider features only within a window. In this paper we propose a method that takes the advantage of recurrent neural network (RNN) to extract higher level features present across the sentence. Thus hidden state representation of RNN along with word and entity type embedding as features avoid relying on the complex hand-crafted features generated using various NLP toolkits. Our experiments have shown to achieve state-of-art F1-score on Multi Level Event Extraction (MLEE) corpus. We have also performed category-wise analysis of the result and discussed the importance of various features in trigger identification task. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1705.09516"
  },
  {
    "id": "6Nw5JrLI",
    "URL": "https://arxiv.org/abs/1706.00125",
    "number": "1706.00125",
    "title": "Convolutional Kitchen Sinks for Transcription Factor Binding Site Prediction",
    "issued": {
      "date-parts": [
        [
          2017,
          6,
          2
        ]
      ]
    },
    "author": [
      {
        "given": "Alyssa",
        "family": "Morrow"
      },
      {
        "given": "Vaishaal",
        "family": "Shankar"
      },
      {
        "given": "Devin",
        "family": "Petersohn"
      },
      {
        "given": "Anthony",
        "family": "Joseph"
      },
      {
        "given": "Benjamin",
        "family": "Recht"
      },
      {
        "given": "Nir",
        "family": "Yosef"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We present a simple and efficient method for prediction of transcription factor binding sites from DNA sequence. Our method computes a random approximation of a convolutional kernel feature map from DNA sequence and then learns a linear model from the approximated feature map. Our method outperforms state-of-the-art deep learning methods on five out of six test datasets from the ENCODE consortium, while training in less than one eighth the time. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1706.00125"
  },
  {
    "id": "TNHJioqT",
    "URL": "https://arxiv.org/abs/1706.01556v2",
    "number": "1706.01556v2",
    "version": "v2",
    "title": "Deep learning for extracting protein-protein interactions from\n  biomedical literature",
    "issued": {
      "date-parts": [
        [
          2017,
          6,
          5
        ]
      ]
    },
    "author": [
      {
        "literal": "Yifan Peng"
      },
      {
        "literal": "Zhiyong Lu"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "State-of-the-art methods for protein-protein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information. But how to incorporate such knowledge in the recent deep learning methods remains an open question. In this paper, we propose a multichannel dependency-based convolutional neural network model (McDepCNN). It applies one channel to the embedding vector of each word in the sentence, and another channel to the embedding vector of the head of the corresponding word. Therefore, the model can use richer information obtained from different channels. Experiments on two public benchmarking datasets, AIMed and BioInfer, demonstrate that McDepCNN compares favorably to the state-of-the-art rich-feature and single-kernel based methods. In addition, McDepCNN achieves 24.4% relative improvement in F1-score over the state-of-the-art methods on cross-corpus evaluation and 12% improvement in F1-score over kernel-based methods on \"difficult\" instances. These results suggest that McDepCNN generalizes more easily over different corpora, and is capable of capturing long distance features in the sentences.",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1706.01556v2"
  },
  {
    "id": "MwUPw4CD",
    "URL": "https://arxiv.org/abs/1706.02216v2",
    "number": "1706.02216v2",
    "version": "v2",
    "title": "Inductive Representation Learning on Large Graphs",
    "issued": {
      "date-parts": [
        [
          2017,
          6,
          7
        ]
      ]
    },
    "author": [
      {
        "literal": "William L. Hamilton"
      },
      {
        "literal": "Rex Ying"
      },
      {
        "literal": "Jure Leskovec"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1706.02216v2"
  },
  {
    "id": "1988BRJe3",
    "URL": "https://arxiv.org/abs/1706.02633v1",
    "number": "1706.02633v1",
    "version": "v1",
    "title": "Real-valued (Medical) Time Series Generation with Recurrent Conditional\n  GANs",
    "issued": {
      "date-parts": [
        [
          2017,
          6,
          8
        ]
      ]
    },
    "author": [
      {
        "literal": "Cristóbal Esteban"
      },
      {
        "literal": "Stephanie L. Hyland"
      },
      {
        "literal": "Gunnar Rätsch"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to produce realistic real-valued multi-dimensional time series, with an emphasis on their application to medical data. RGANs make use of recurrent neural networks in the generator and the discriminator. In the case of RCGANs, both of these RNNs are conditioned on auxiliary information. We demonstrate our models in a set of toy datasets, where we show visually and quantitatively (using sample likelihood and maximum mean discrepancy) that they can successfully generate realistic time-series. We also describe novel evaluation methods for GANs, where we generate a synthetic labelled training dataset, and evaluate on a real test set the performance of a model trained on the synthetic data, and vice-versa. We illustrate with these metrics that RCGANs can generate time-series data useful for supervised training, with only minor degradation in performance on real test data. This is demonstrated on digit classification from 'serialised' MNIST and by training an early warning system on a medical dataset of 17,000 patients from an intensive care unit. We further discuss and analyse the privacy concerns that may arise when using RCGANs to generate realistic synthetic medical time series data.",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1706.02633v1"
  },
  {
    "id": "vJxaHm0b",
    "URL": "https://arxiv.org/abs/1706.02690",
    "number": "1706.02690",
    "title": "Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks",
    "issued": {
      "date-parts": [
        [
          2018,
          2,
          27
        ]
      ]
    },
    "author": [
      {
        "given": "Shiyu",
        "family": "Liang"
      },
      {
        "given": "Yixuan",
        "family": "Li"
      },
      {
        "given": "R.",
        "family": "Srikant"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does not require any change to a pre-trained neural network. Our method is based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions between in- and out-of-distribution images, allowing for more effective detection. We show in a series of experiments that ODIN is compatible with diverse network architectures and datasets. It consistently outperforms the baseline approach by a large margin, establishing a new state-of-the-art performance on this task. For example, ODIN reduces the false positive rate from the baseline 34.7% to 4.3% on the DenseNet (applied to CIFAR-10) when the true positive rate is 95%. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1706.02690"
  },
  {
    "id": "QJ6hYH8N",
    "URL": "https://arxiv.org/abs/1706.04599",
    "number": "1706.04599",
    "title": "On Calibration of Modern Neural Networks",
    "issued": {
      "date-parts": [
        [
          2017,
          8,
          4
        ]
      ]
    },
    "author": [
      {
        "given": "Chuan",
        "family": "Guo"
      },
      {
        "given": "Geoff",
        "family": "Pleiss"
      },
      {
        "given": "Yu",
        "family": "Sun"
      },
      {
        "given": "Kilian Q.",
        "family": "Weinberger"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1706.04599"
  },
  {
    "id": "PMuw2Jdj",
    "URL": "https://arxiv.org/abs/1706.06689",
    "number": "1706.06689",
    "title": "Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models",
    "issued": {
      "date-parts": [
        [
          2018,
          8,
          15
        ]
      ]
    },
    "author": [
      {
        "given": "Garrett B.",
        "family": "Goh"
      },
      {
        "given": "Charles",
        "family": "Siegel"
      },
      {
        "given": "Abhinav",
        "family": "Vishnu"
      },
      {
        "given": "Nathan O.",
        "family": "Hodas"
      },
      {
        "given": "Nathan",
        "family": "Baker"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  In the last few years, we have seen the transformative impact of deep learning in many applications, particularly in speech recognition and computer vision. Inspired by Google's Inception-ResNet deep convolutional neural network (CNN) for image classification, we have developed \"Chemception\", a deep CNN for the prediction of chemical properties, using just the images of 2D drawings of molecules. We develop Chemception without providing any additional explicit chemistry knowledge, such as basic concepts like periodicity, or advanced features like molecular descriptors and fingerprints. We then show how Chemception can serve as a general-purpose neural network architecture for predicting toxicity, activity, and solvation properties when trained on a modest database of 600 to 40,000 compounds. When compared to multi-layer perceptron (MLP) deep neural networks trained with ECFP fingerprints, Chemception slightly outperforms in activity and solvation prediction and slightly underperforms in toxicity prediction. Having matched the performance of expert-developed QSAR/QSPR deep learning models, our work demonstrates the plausibility of using deep neural networks to assist in computational chemistry research, where the feature engineering process is performed primarily by a deep learning algorithm. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1706.06689"
  },
  {
    "id": "P9vKrhYz",
    "URL": "https://arxiv.org/abs/1707.02476",
    "number": "1707.02476",
    "title": "Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks",
    "issued": {
      "date-parts": [
        [
          2017,
          7,
          11
        ]
      ]
    },
    "author": [
      {
        "given": "John",
        "family": "Bradshaw"
      },
      {
        "given": "Alexander G. de G.",
        "family": "Matthews"
      },
      {
        "given": "Zoubin",
        "family": "Ghahramani"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Deep neural networks (DNNs) have excellent representative power and are state of the art classifiers on many tasks. However, they often do not capture their own uncertainties well making them less robust in the real world as they overconfidently extrapolate and do not notice domain shift. Gaussian processes (GPs) with RBF kernels on the other hand have better calibrated uncertainties and do not overconfidently extrapolate far from data in their training set. However, GPs have poor representational power and do not perform as well as DNNs on complex domains. In this paper we show that GP hybrid deep networks, GPDNNs, (GPs on top of DNNs and trained end-to-end) inherit the nice properties of both GPs and DNNs and are much more robust to adversarial examples. When extrapolating to adversarial examples and testing in domain shift settings, GPDNNs frequently output high entropy class probabilities corresponding to essentially \"don't know\". GPDNNs are therefore promising as deep architectures that know when they don't know. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1707.02476"
  },
  {
    "id": "16MNknNBL",
    "URL": "https://arxiv.org/abs/1708.00339",
    "number": "1708.00339",
    "title": "Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin",
    "issued": {
      "date-parts": [
        [
          2017,
          11,
          8
        ]
      ]
    },
    "author": [
      {
        "given": "Ritambhara",
        "family": "Singh"
      },
      {
        "given": "Jack",
        "family": "Lanchantin"
      },
      {
        "given": "Arshdeep",
        "family": "Sekhon"
      },
      {
        "given": "Yanjun",
        "family": "Qi"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  The past decade has seen a revolution in genomic technologies that enable a flood of genome-wide profiling of chromatin marks. Recent literature tried to understand gene regulation by predicting gene expression from large-scale chromatin measurements. Two fundamental challenges exist for such learning tasks: (1) genome-wide chromatin signals are spatially structured, high-dimensional and highly modular; and (2) the core aim is to understand what are the relevant factors and how they work together? Previous studies either failed to model complex dependencies among input signals or relied on separate feature analysis to explain the decisions. This paper presents an attention-based deep learning approach; we call AttentiveChrome, that uses a unified architecture to model and to interpret dependencies among chromatin factors for controlling gene regulation. AttentiveChrome uses a hierarchy of multiple Long short-term memory (LSTM) modules to encode the input signals and to model how various chromatin marks cooperate automatically. AttentiveChrome trains two levels of attention jointly with the target prediction, enabling it to attend differentially to relevant marks and to locate important positions per mark. We evaluate the model across 56 different cell types (tasks) in human. Not only is the proposed architecture more accurate, but its attention scores also provide a better interpretation than state-of-the-art feature visualization methods such as saliency map.\n  Code and data are shared at www.deepchrome.org ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1708.00339"
  },
  {
    "id": "zBCcUQOM",
    "URL": "https://arxiv.org/abs/1708.04692",
    "number": "1708.04692",
    "title": "GANs for Biological Image Synthesis",
    "issued": {
      "date-parts": [
        [
          2017,
          9,
          13
        ]
      ]
    },
    "author": [
      {
        "given": "Anton",
        "family": "Osokin"
      },
      {
        "given": "Anatole",
        "family": "Chessel"
      },
      {
        "given": "Rafael E. Carazo",
        "family": "Salas"
      },
      {
        "given": "Federico",
        "family": "Vaggi"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  In this paper, we propose a novel application of Generative Adversarial Networks (GAN) to the synthesis of cells imaged by fluorescence microscopy. Compared to natural images, cells tend to have a simpler and more geometric global structure that facilitates image generation. However, the correlation between the spatial pattern of different fluorescent proteins reflects important biological functions, and synthesized images have to capture these relationships to be relevant for biological applications. We adapt GANs to the task at hand and propose new models with casual dependencies between image channels that can generate multi-channel images, which would be impossible to obtain experimentally. We evaluate our approach using two independent techniques and compare it against sensible baselines. Finally, we demonstrate that by interpolating across the latent space we can mimic the known changes in protein localization that occur through time during the cell cycle, allowing us to predict temporal evolution from static images. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1708.04692"
  },
  {
    "id": "RHqbJgpe",
    "URL": "https://arxiv.org/abs/1709.02082",
    "number": "1709.02082",
    "title": "A deep generative model for gene expression profiles from single-cell RNA sequencing",
    "issued": {
      "date-parts": [
        [
          2018,
          1,
          18
        ]
      ]
    },
    "author": [
      {
        "given": "Romain",
        "family": "Lopez"
      },
      {
        "given": "Jeffrey",
        "family": "Regier"
      },
      {
        "given": "Michael",
        "family": "Cole"
      },
      {
        "given": "Michael",
        "family": "Jordan"
      },
      {
        "given": "Nir",
        "family": "Yosef"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the model, each cell has a low-dimensional latent representation. Additional latent variables account for technical effects that may erroneously set some observations of gene expression levels to zero. Conditional distributions are specified by neural networks, giving the proposed model enough flexibility to fit the data well. We use variational inference and stochastic optimization to approximate the posterior distribution. The inference procedure scales to over one million cells, whereas competing algorithms do not. Even for smaller datasets, for several tasks, the proposed procedure outperforms state-of-the-art methods like ZIFA and ZINB-WaVE. We also extend our framework to account for batch effects and other confounding factors, and propose a Bayesian hypothesis test for differential expression that outperforms DESeq2. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1709.02082"
  },
  {
    "id": "emWW7yUp",
    "URL": "https://arxiv.org/abs/1710.04759",
    "number": "1710.04759",
    "title": "Bayesian Hypernetworks",
    "issued": {
      "date-parts": [
        [
          2018,
          4,
          26
        ]
      ]
    },
    "author": [
      {
        "given": "David",
        "family": "Krueger"
      },
      {
        "given": "Chin-Wei",
        "family": "Huang"
      },
      {
        "given": "Riashat",
        "family": "Islam"
      },
      {
        "given": "Ryan",
        "family": "Turner"
      },
      {
        "given": "Alexandre",
        "family": "Lacoste"
      },
      {
        "given": "Aaron",
        "family": "Courville"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  We study Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork $\\h$ is a neural network which learns to transform a simple noise distribution, $p(\\vec\\epsilon) = \\N(\\vec 0,\\mat I)$, to a distribution $q(\\pp) := q(h(\\vec\\epsilon))$ over the parameters $\\pp$ of another neural network (the \"primary network\")\\@. We train $q$ with variational inference, using an invertible $\\h$ to enable efficient estimation of the variational lower bound on the posterior $p(\\pp | \\D)$ via sampling. In contrast to most methods for Bayesian deep learning, Bayesian hypernets can represent a complex multimodal approximate posterior with correlations between parameters, while enabling cheap iid sampling of~$q(\\pp)$. In practice, Bayesian hypernets can provide a better defense against adversarial examples than dropout, and also exhibit competitive performance on a suite of tasks which evaluate model uncertainty, including regularization, active learning, and anomaly detection. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1710.04759"
  },
  {
    "id": "FMsqNIQ4",
    "URL": "https://arxiv.org/abs/1710.10568v1",
    "number": "1710.10568v1",
    "version": "v1",
    "title": "Stochastic Training of Graph Convolutional Networks",
    "issued": {
      "date-parts": [
        [
          2017,
          10,
          29
        ]
      ]
    },
    "author": [
      {
        "literal": "Jianfei Chen"
      },
      {
        "literal": "Jun Zhu"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "Graph convolutional networks (GCNs) are powerful deep neural networks for graph-structured data. However, GCN computes nodes' representation recursively from their neighbors, making the receptive field size grow exponentially with the number of layers. Previous attempts on reducing the receptive field size by subsampling neighbors do not have any convergence guarantee, and their receptive field size per node is still in the order of hundreds. In this paper, we develop a preprocessing strategy and two control variate based algorithms to further reduce the receptive field size. Our algorithms are guaranteed to converge to GCN's local optimum regardless of the neighbor sampling size. Empirical results show that our algorithms have a similar convergence speed per epoch with the exact algorithm even using only two neighbors per node. The time consumption of our algorithm on the Reddit dataset is only one fifth of previous neighbor sampling algorithms.",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1710.10568v1"
  },
  {
    "id": "rMz1OFc6",
    "URL": "https://arxiv.org/abs/1711.04828",
    "number": "1711.04828",
    "title": "Evaluating deep variational autoencoders trained on pan-cancer gene expression",
    "issued": {
      "date-parts": [
        [
          2017,
          11,
          15
        ]
      ]
    },
    "author": [
      {
        "given": "Gregory P.",
        "family": "Way"
      },
      {
        "given": "Casey S.",
        "family": "Greene"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Cancer is a heterogeneous disease with diverse molecular etiologies and outcomes. The Cancer Genome Atlas (TCGA) has released a large compendium of over 10,000 tumors with RNA-seq gene expression measurements. Gene expression captures the diverse molecular profiles of tumors and can be interrogated to reveal differential pathway activations. Deep unsupervised models, including Variational Autoencoders (VAE) can be used to reveal these underlying patterns. We compare a one-hidden layer VAE to two alternative VAE architectures with increased depth. We determine the additional capacity marginally improves performance. We train and compare the three VAE architectures to other dimensionality reduction techniques including principal components analysis (PCA), independent components analysis (ICA), non-negative matrix factorization (NMF), and analysis of gene expression by denoising autoencoders (ADAGE). We compare performance in a supervised learning task predicting gene inactivation pan-cancer and in a latent space analysis of high grade serous ovarian cancer (HGSC) subtypes. We do not observe substantial differences across algorithms in the classification task. VAE latent spaces offer biological insights into HGSC subtype biology. ",
    "note": "license: http://creativecommons.org/licenses/by/4.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1711.04828"
  },
  {
    "id": "gYxBO26g",
    "URL": "https://arxiv.org/abs/1712.05898",
    "number": "1712.05898",
    "title": "NegBio: a high-performance tool for negation and uncertainty detection in radiology reports",
    "issued": {
      "date-parts": [
        [
          2017,
          12,
          29
        ]
      ]
    },
    "author": [
      {
        "given": "Yifan",
        "family": "Peng"
      },
      {
        "given": "Xiaosong",
        "family": "Wang"
      },
      {
        "given": "Le",
        "family": "Lu"
      },
      {
        "given": "Mohammadhadi",
        "family": "Bagheri"
      },
      {
        "given": "Ronald",
        "family": "Summers"
      },
      {
        "given": "Zhiyong",
        "family": "Lu"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Negative and uncertain medical findings are frequent in radiology reports, but discriminating them from positive findings remains challenging for information extraction. Here, we propose a new algorithm, NegBio, to detect negative and uncertain findings in radiology reports. Unlike previous rule-based methods, NegBio utilizes patterns on universal dependencies to identify the scope of triggers that are indicative of negation or uncertainty. We evaluated NegBio on four datasets, including two public benchmarking corpora of radiology reports, a new radiology corpus that we annotated for this work, and a public corpus of general clinical texts. Evaluation on these datasets demonstrates that NegBio is highly accurate for detecting negative and uncertain findings and compares favorably to a widely-used state-of-the-art system NegEx (an average of 9.5% improvement in precision and 5.1% in F1-score). ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1712.05898"
  },
  {
    "id": "nMXQBadV",
    "URL": "https://arxiv.org/abs/1801.02144",
    "number": "1801.02144",
    "title": "Covariant Compositional Networks For Learning Graphs",
    "issued": {
      "date-parts": [
        [
          2018,
          1,
          9
        ]
      ]
    },
    "author": [
      {
        "given": "Risi",
        "family": "Kondor"
      },
      {
        "given": "Hy Truong",
        "family": "Son"
      },
      {
        "given": "Horace",
        "family": "Pan"
      },
      {
        "given": "Brandon",
        "family": "Anderson"
      },
      {
        "given": "Shubhendu",
        "family": "Trivedi"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Most existing neural networks for learning graphs address permutation invariance by conceiving of the network as a message passing scheme, where each node sums the feature vectors coming from its neighbors. We argue that this imposes a limitation on their representation power, and instead propose a new general architecture for representing objects consisting of a hierarchy of parts, which we call Covariant Compositional Networks (CCNs). Here, covariance means that the activation of each neuron must transform in a specific way under permutations, similarly to steerability in CNNs. We achieve covariance by making each activation transform according to a tensor representation of the permutation group, and derive the corresponding tensor aggregation rules that each neuron must implement. Experiments show that CCNs can outperform competing methods on standard graph learning benchmarks. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1801.02144"
  },
  {
    "id": "brPjEjYw",
    "URL": "https://arxiv.org/abs/1801.08570",
    "number": "1801.08570",
    "title": "Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification",
    "issued": {
      "date-parts": [
        [
          2018,
          5,
          1
        ]
      ]
    },
    "author": [
      {
        "given": "Alexandr A.",
        "family": "Kalinin"
      },
      {
        "given": "Gerald A.",
        "family": "Higgins"
      },
      {
        "given": "Narathip",
        "family": "Reamaroon"
      },
      {
        "given": "S. M. Reza",
        "family": "Soroushmehr"
      },
      {
        "given": "Ari",
        "family": "Allyn-Feuer"
      },
      {
        "given": "Ivo D.",
        "family": "Dinov"
      },
      {
        "given": "Kayvan",
        "family": "Najarian"
      },
      {
        "given": "Brian D.",
        "family": "Athey"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: (1) identification of novel regulatory variants located in noncoding domains and their function as applied to pharmacoepigenomics; (2) patient stratification from medical records; and (3) prediction of drugs, targets, and their interactions. Deep learning encapsulates a family of machine learning algorithms that over the last decade has transformed many important subfields of artificial intelligence (AI) and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical, and demographic datasets. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1801.08570",
    "DOI": "10.2217/pgs-2018-0008"
  },
  {
    "id": "GLXmFOus",
    "URL": "https://arxiv.org/abs/1807.09617",
    "number": "1807.09617",
    "title": "Convolutional Neural Networks In Classifying Cancer Through DNA Methylation",
    "issued": {
      "date-parts": [
        [
          2018,
          7,
          26
        ]
      ]
    },
    "author": [
      {
        "given": "Soham",
        "family": "Chatterjee"
      },
      {
        "given": "Archana",
        "family": "Iyer"
      },
      {
        "given": "Satya",
        "family": "Avva"
      },
      {
        "given": "Abhai",
        "family": "Kollara"
      },
      {
        "given": "Malaikannan",
        "family": "Sankarasubbu"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  DNA Methylation has been the most extensively studied epigenetic mark. Usually a change in the genotype, DNA sequence, leads to a change in the phenotype, observable characteristics of the individual. But DNA methylation, which happens in the context of CpG (cytosine and guanine bases linked by phosphate backbone) dinucleotides, does not lead to a change in the original DNA sequence but has the potential to change the phenotype. DNA methylation is implicated in various biological processes and diseases including cancer. Hence there is a strong interest in understanding the DNA methylation patterns across various epigenetic related ailments in order to distinguish and diagnose the type of disease in its early stages. In this work, the relationship between methylated versus unmethylated CpG regions and cancer types is explored using Convolutional Neural Networks (CNNs). A CNN based Deep Learning model that can classify the cancer of a new DNA methylation profile based on the learning from publicly available DNA methylation datasets is then proposed. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1807.09617"
  },
  {
    "id": "1CvlEI6Kb",
    "URL": "https://arxiv.org/abs/1808.01359",
    "number": "1808.01359",
    "title": "Deep Neural Network for Analysis of DNA Methylation Data",
    "issued": {
      "date-parts": [
        [
          2020,
          1,
          29
        ]
      ]
    },
    "author": [
      {
        "given": "Hong",
        "family": "Yu"
      },
      {
        "given": "Zhanyu",
        "family": "Ma"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  Many researches demonstrated that the DNA methylation, which occurs in the context of a CpG, has strong correlation with diseases, including cancer. There is a strong interest in analyzing the DNA methylation data to find how to distinguish different subtypes of the tumor. However, the conventional statistical methods are not suitable for analyzing the highly dimensional DNA methylation data with bounded support. In order to explicitly capture the properties of the data, we design a deep neural network, which composes of several stacked binary restricted Boltzmann machines, to learn the low dimensional deep features of the DNA methylation data. Experiments show these features perform best in breast cancer DNA methylation data cluster analysis, comparing with some state-of-the-art methods. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1808.01359"
  },
  {
    "id": "EXGRa0DD",
    "URL": "https://arxiv.org/abs/1810.01243",
    "number": "1810.01243",
    "title": "A Deep Autoencoder System for Differentiation of Cancer Types Based on DNA Methylation State",
    "issued": {
      "date-parts": [
        [
          2018,
          10,
          8
        ]
      ]
    },
    "author": [
      {
        "given": "Mohammed",
        "family": "Khwaja"
      },
      {
        "given": "Melpomeni",
        "family": "Kalofonou"
      },
      {
        "given": "Chris",
        "family": "Toumazou"
      }
    ],
    "container-title": "arXiv",
    "publisher": "arXiv",
    "type": "report",
    "abstract": "  A Deep Autoencoder based content retrieval algorithm is proposed for prediction and differentiation of cancer types based on the presence of epigenetic patterns of DNA methylation identified in genetic regions known as CpG islands. The developed deep learning system uses a CpG island state classification sub-system to complete sets of missing/incomplete island data in given human cell lines, and is then pipelined with an intricate set of statistical and signal processing methods to accurately predict the presence of cancer and further differentiate the type and cell of origin in the event of a positive result. The proposed system was trained with previously reported data derived from four case groups of cancer cell lines, achieving overall Sensitivity of 88.24%, Specificity of 83.33%, Accuracy of 84.75% and Matthews Correlation Coefficient of 0.687. The ability to predict and differentiate cancer types using epigenetic events as the identifying patterns was demonstrated in previously reported data sets from breast, lung, lymphoblastic leukemia and urological cancer cell lines, allowing the pipelined system to be robust and adjustable to other cancer cell lines or epigenetic events. ",
    "note": "license: http://arxiv.org/licenses/nonexclusive-distrib/1.0/\nThis CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: arxiv:1810.01243"
  },
  {
    "type": "article-journal",
    "id": "1mJW6umJ",
    "author": [
      {
        "family": "Gulshan",
        "given": "Varun"
      },
      {
        "family": "Peng",
        "given": "Lily"
      },
      {
        "family": "Coram",
        "given": "Marc"
      },
      {
        "family": "Stumpe",
        "given": "Martin C."
      },
      {
        "family": "Wu",
        "given": "Derek"
      },
      {
        "family": "Narayanaswamy",
        "given": "Arunachalam"
      },
      {
        "family": "Venugopalan",
        "given": "Subhashini"
      },
      {
        "family": "Widner",
        "given": "Kasumi"
      },
      {
        "family": "Madams",
        "given": "Tom"
      },
      {
        "family": "Cuadros",
        "given": "Jorge"
      },
      {
        "family": "Kim",
        "given": "Ramasamy"
      },
      {
        "family": "Raman",
        "given": "Rajiv"
      },
      {
        "family": "Nelson",
        "given": "Philip C."
      },
      {
        "family": "Mega",
        "given": "Jessica L."
      },
      {
        "family": "Webster",
        "given": "Dale R."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          12,
          13
        ]
      ]
    },
    "container-title": "JAMA",
    "DOI": "10.1001/jama.2016.17216",
    "volume": "316",
    "issue": "22",
    "page": "2402",
    "publisher": "American Medical Association (AMA)",
    "title": "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs",
    "URL": "https://doi.org/gcgk7d",
    "PMID": "27898976",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1001/jama.2016.17216"
  },
  {
    "type": "book",
    "id": "17eGl2pn9",
    "author": [
      {
        "family": "Todeschini",
        "given": "Roberto"
      },
      {
        "family": "Consonni",
        "given": "Viviana"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2009,
          7,
          15
        ]
      ]
    },
    "container-title": "Methods and Principles in Medicinal Chemistry",
    "DOI": "10.1002/9783527628766",
    "volume": "41",
    "publisher": "Wiley",
    "title": "Molecular Descriptors for Chemoinformatics",
    "URL": "https://doi.org/c9xk4r",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1002/9783527628766"
  },
  {
    "type": "article-journal",
    "id": "m3Ij21U8",
    "author": [
      {
        "family": "Bougen-Zhukov",
        "given": "Nicola"
      },
      {
        "family": "Loh",
        "given": "Sheng Yang"
      },
      {
        "family": "Lee",
        "given": "Hwee Kuan"
      },
      {
        "family": "Loo",
        "given": "Lit-Hsin"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          2
        ]
      ]
    },
    "container-title": "Cytometry Part A",
    "DOI": "10.1002/cyto.a.22909",
    "volume": "91",
    "issue": "2",
    "page": "115-125",
    "publisher": "Wiley",
    "title": "Large-scale image-based screening and profiling of cellular phenotypes",
    "URL": "https://doi.org/f9rp9b",
    "PMID": "27434125",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1002/cyto.a.22909"
  },
  {
    "type": "article-journal",
    "id": "zCt6PUXj",
    "author": [
      {
        "family": "Goh",
        "given": "Garrett B."
      },
      {
        "family": "Hodas",
        "given": "Nathan O."
      },
      {
        "family": "Vishnu",
        "given": "Abhinav"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          6,
          15
        ]
      ]
    },
    "container-title": "Journal of Computational Chemistry",
    "DOI": "10.1002/jcc.24764",
    "volume": "38",
    "issue": "16",
    "page": "1291-1307",
    "publisher": "Wiley",
    "title": "Deep learning for computational chemistry",
    "URL": "https://doi.org/f959s6",
    "PMID": "28272810",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1002/jcc.24764"
  },
  {
    "type": "article-journal",
    "id": "gJE0ExFr",
    "author": [
      {
        "family": "Gawehn",
        "given": "Erik"
      },
      {
        "family": "Hiss",
        "given": "Jan A."
      },
      {
        "family": "Schneider",
        "given": "Gisbert"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          1
        ]
      ]
    },
    "container-title": "Molecular Informatics",
    "DOI": "10.1002/minf.201501008",
    "volume": "35",
    "issue": "1",
    "page": "3-14",
    "publisher": "Wiley",
    "title": "Deep Learning in Drug Discovery",
    "URL": "https://doi.org/f3kg55",
    "PMID": "27491648",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1002/minf.201501008"
  },
  {
    "type": "article-journal",
    "id": "x0M6vals",
    "author": [
      {
        "family": "Hamanaka",
        "given": "Masatoshi"
      },
      {
        "family": "Taneishi",
        "given": "Kei"
      },
      {
        "family": "Iwata",
        "given": "Hiroaki"
      },
      {
        "family": "Ye",
        "given": "Jun"
      },
      {
        "family": "Pei",
        "given": "Jianguo"
      },
      {
        "family": "Hou",
        "given": "Jinlong"
      },
      {
        "family": "Okuno",
        "given": "Yasushi"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          8,
          12
        ]
      ]
    },
    "container-title": "Molecular Informatics",
    "DOI": "10.1002/minf.201600045",
    "volume": "36",
    "issue": "1-2",
    "page": "1600045",
    "publisher": "Wiley",
    "title": "CGBVS‐DNN: Prediction of Compound‐protein Interactions Based on Deep Learning",
    "URL": "https://doi.org/f3q59v",
    "PMID": "27515489",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1002/minf.201600045"
  },
  {
    "type": "article-journal",
    "id": "WeiyYhfy",
    "author": [
      {
        "family": "Reymond",
        "given": "Jean-Louis"
      },
      {
        "family": "Ruddigkeit",
        "given": "Lars"
      },
      {
        "family": "Blum",
        "given": "Lorenz"
      },
      {
        "family": "van Deursen",
        "given": "Ruud"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2012,
          9
        ]
      ]
    },
    "container-title": "Wiley Interdisciplinary Reviews: Computational Molecular Science",
    "DOI": "10.1002/wcms.1104",
    "volume": "2",
    "issue": "5",
    "page": "717-733",
    "publisher": "Wiley",
    "title": "The enumeration of chemical space",
    "URL": "https://doi.org/gcgk7f",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1002/wcms.1104"
  },
  {
    "type": "article-journal",
    "id": "kJ4hy7E",
    "author": [
      {
        "family": "Hartenfeller",
        "given": "Markus"
      },
      {
        "family": "Schneider",
        "given": "Gisbert"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2011,
          4,
          25
        ]
      ]
    },
    "container-title": "WIREs Computational Molecular Science",
    "DOI": "10.1002/wcms.49",
    "volume": "1",
    "issue": "5",
    "page": "742-759",
    "publisher": "Wiley",
    "title": "Enabling future drug discovery by <i>de novo</i> design",
    "URL": "https://doi.org/bv7hkf",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1002/wcms.49"
  },
  {
    "type": "article-journal",
    "id": "Aic7UyXM",
    "author": [
      {
        "family": "Jones",
        "given": "David T"
      }
    ],
    "issued": {
      "date-parts": [
        [
          1999,
          9
        ]
      ]
    },
    "container-title": "Journal of Molecular Biology",
    "DOI": "10.1006/jmbi.1999.3091",
    "volume": "292",
    "issue": "2",
    "page": "195-202",
    "publisher": "Elsevier BV",
    "title": "Protein secondary structure prediction based on position-specific scoring matrices 1 1Edited by G. Von Heijne",
    "URL": "https://doi.org/d3fxv7",
    "PMID": "10493868",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1006/jmbi.1999.3091"
  },
  {
    "type": "chapter",
    "id": "17B2QAA1k",
    "author": [
      {
        "family": "Li",
        "given": "Yifeng"
      },
      {
        "family": "Chen",
        "given": "Chih-Yu"
      },
      {
        "family": "Wasserman",
        "given": "Wyeth W."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2015
        ]
      ]
    },
    "container-title": "Lecture Notes in Computer Science",
    "DOI": "10.1007/978-3-319-16706-0_20",
    "publisher": "Springer International Publishing",
    "title": "Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters",
    "URL": "https://doi.org/gcgk7g",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1007/978-3-319-16706-0_20"
  },
  {
    "type": "chapter",
    "id": "zYUI7tc1",
    "author": [
      {
        "family": "Sønderby",
        "given": "Søren Kaae"
      },
      {
        "family": "Sønderby",
        "given": "Casper Kaae"
      },
      {
        "family": "Nielsen",
        "given": "Henrik"
      },
      {
        "family": "Winther",
        "given": "Ole"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2015
        ]
      ]
    },
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    "container-title": "Proceedings of the National Academy of Sciences",
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    "volume": "100",
    "issue": "26",
    "page": "15776-15781",
    "publisher": "Proceedings of the National Academy of Sciences",
    "title": "Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage",
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        "given": "Ben T."
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      {
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      {
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    "container-title": "Briefings in Bioinformatics",
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          29
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    "container-title": "Bioinformatics",
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    ],
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      ]
    },
    "container-title": "Nucleic Acids Research",
    "DOI": "10.1093/nar/gkx681",
    "volume": "45",
    "issue": "17",
    "page": "e156-e156",
    "publisher": "Oxford University Press (OUP)",
    "title": "Using neural networks for reducing the dimensions of single-cell RNA-Seq data",
    "URL": "https://doi.org/gcnzb7",
    "PMCID": "PMC5737331",
    "PMID": "28973464",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1093/nar/gkx681"
  },
  {
    "type": "article-journal",
    "id": "AnenJOuU",
    "author": [
      {
        "family": "Gupta",
        "given": "Aman"
      },
      {
        "family": "Wang",
        "given": "Haohan"
      },
      {
        "family": "Ganapathiraju",
        "given": "Madhavi"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2015,
          11,
          16
        ]
      ]
    },
    "abstract": "Genes play a central role in all biological processes. DNA microarray technology has made it possible to study the expression behavior of thousands of genes in one go. Often, gene expression data is used to generate features for supervised and unsupervised learning tasks. At the same time, advances in the field of deep learning have made available a plethora of architectures. In this paper, we use deep architectures pre-trained in an unsupervised manner using denoising autoencoders as a preprocessing step for a popular unsupervised learning task. Denoising autoencoders (DA) can be used to learn a compact representation of input, and have been used to generate features for further supervised learning tasks. We propose that our deep architectures can be treated as empirical versions of Deep Belief Networks (DBNs). We use our deep architectures to regenerate gene expression time series data for two different data sets. We test our hypothesis on two popular datasets for the unsupervised learning task of clustering and find promising improvements in performance.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/031906",
    "publisher": "bioRxiv",
    "title": "Learning structure in gene expression data using deep architectures, with an application to gene clustering",
    "URL": "https://doi.org/gcgk84",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/031906"
  },
  {
    "type": "article-journal",
    "id": "s5sy4AOi",
    "author": [
      {
        "family": "Liu",
        "given": "Feng"
      },
      {
        "family": "Li",
        "given": "Hao"
      },
      {
        "family": "Ren",
        "given": "Chao"
      },
      {
        "family": "Bo",
        "given": "Xiaochen"
      },
      {
        "family": "Shu",
        "given": "Wenjie"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          5,
          18
        ]
      ]
    },
    "abstract": "Transcriptional enhancers are non-coding segments of DNA that play a central role in the spatiotemporal regulation of gene expression programs. However, systematically and precisely predicting enhancers remain a major challenge. Although existing methods have achieved some success in enhancer prediction, they still suffer from many issues. We developed a deep learning-based algorithmic framework named PEDLA ( ), which can directly learn an enhancer predictor from massively heterogeneous data and generalize in ways that are mostly consistent across various cell types/tissues. We first trained PEDLA with 1,114-dimensional heterogeneous features in H1 cells, and we demonstrated that our PEDLA framework integrates diverse heterogeneous features and gives state-of-the-art performance relative to five existing methods for enhancer prediction. We further extended PEDLA to iteratively learn from 22 training cell types/tissues. Our results showed that PEDLA manifested superior performance consistency in both training and independent test sets. On average, PEDLA achieved 95.0% accuracy and a 96.8% geometric mean (GM) across 22 training cell types/tissues, as well as 95.7% accuracy and a 96.8% GM across 20 independent test cell types/tissues. Together, our work illustrates the power of harnessing state-of-the-art deep learning techniques to consistently identify regulatory elements at a genome-wide scale from massively heterogeneous data across diverse cell types/tissues.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/036129",
    "publisher": "bioRxiv",
    "title": "PEDLA: predicting enhancers with a deep learning-based algorithmic framework",
    "URL": "https://doi.org/gcgk85",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/036129"
  },
  {
    "type": "article-journal",
    "id": "1HbQQcY2q",
    "author": [
      {
        "family": "Li",
        "given": "Yifeng"
      },
      {
        "family": "Shi",
        "given": "Wenqiang"
      },
      {
        "family": "Wasserman",
        "given": "Wyeth W"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          2,
          28
        ]
      ]
    },
    "abstract": "Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES, the first supervised deep learning approach for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data) and 26,000 candidate promoters (0.6% of the genome).",
    "container-title": "bioRxiv",
    "DOI": "10.1101/041616",
    "publisher": "bioRxiv",
    "title": "Genome-Wide Prediction of cis-Regulatory Regions Using Supervised Deep Learning Methods",
    "URL": "https://doi.org/gcgk86",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/041616"
  },
  {
    "type": "article-journal",
    "id": "r3Gbjksq",
    "author": [
      {
        "family": "Arvaniti",
        "given": "Eirini"
      },
      {
        "family": "Claassen",
        "given": "Manfred"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          3,
          31
        ]
      ]
    },
    "abstract": "Rare cell populations play a pivotal role in the initiation and progression of diseases like cancer. However, the identification of such subpopulations remains a difficult task. This work describes CellCnn, a representation learning approach to detect rare cell subsets associated with disease using high dimensional single cell measurements. Using CellCnn, we identify paracrine signaling and AIDS onset associated cell subsets in peripheral blood, and minimal residual disease associated populations in leukemia with frequencies as low as 0.005%.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/046508",
    "publisher": "bioRxiv",
    "title": "Sensitive detection of rare disease-associated cell subsets via representation learning",
    "URL": "https://doi.org/gcgk87",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/046508"
  },
  {
    "type": "article-journal",
    "id": "XimuXZlz",
    "author": [
      {
        "family": "Koh",
        "given": "Pang Wei"
      },
      {
        "family": "Pierson",
        "given": "Emma"
      },
      {
        "family": "Kundaje",
        "given": "Anshul"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          1,
          27
        ]
      ]
    },
    "container-title": "bioRxiv",
    "DOI": "10.1101/052118",
    "publisher": "bioRxiv",
    "title": "Denoising Genome-wide Histone ChIP-seq with Convolutional Neural Networks",
    "URL": "https://doi.org/gcgk88",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/052118"
  },
  {
    "type": "article-journal",
    "id": "1Hk3NTSn2",
    "author": [
      {
        "family": "Rubinsteyn",
        "given": "Alex"
      },
      {
        "family": "O'Donnell",
        "given": "Timothy"
      },
      {
        "family": "Damaraju",
        "given": "Nandita"
      },
      {
        "family": "Hammerbacher",
        "given": "Jeff"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          6,
          7
        ]
      ]
    },
    "abstract": "Predicting the binding affinity between MHC proteins and their peptide ligands is a key problem in computational immunology. State of the art performance is currently achieved by the allele-specific predictor NetMHC and the pan-allele predictor NetMHCpan, both of which are ensembles of shallow neural networks. We explore an intermediate between allele-specific and pan-allele prediction: training allele-specific predictors with synthetic samples generated by imputation of the peptide-MHC affinity matrix. We find that the imputation strategy is useful on alleles with very little training data. We have implemented our predictor as an open-source software package called MHCflurry and show that MHCflurry achieves competitive performance to NetMHC and NetMHCpan.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/054775",
    "publisher": "bioRxiv",
    "title": "Predicting Peptide-MHC Binding Affinities with Imputed Training Data",
    "URL": "https://doi.org/gcgk89",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/054775"
  },
  {
    "type": "article-journal",
    "id": "11NHbWB1V",
    "author": [
      {
        "family": "Sarraf",
        "given": "Saman"
      },
      {
        "family": "DeSouza",
        "given": "Danielle D."
      },
      {
        "family": "Anderson",
        "given": "John"
      },
      {
        "family": "Tofighi",
        "given": "Ghassem"
      },
      {
        "literal": "for the Alzheimer's Disease Neuroimaging Initiativ"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          1,
          14
        ]
      ]
    },
    "abstract": "To extract patterns from neuroimaging data, various statistical methods and machine learning algorithms have been explored for the diagnosis of Alzheimer’s disease among older adults in both clinical and research applications; however, distinguishing between Alzheimer’s and healthy brain data has been challenging in older adults (age &gt; 75) due to highly similar patterns of brain atrophy and image intensities. Recently, cutting-edge deep learning technologies have rapidly expanded into numerous fields, including medical image analysis. This paper outlines state-of-the-art deep learning-based pipelines employed to distinguish Alzheimer’s magnetic resonance imaging (MRI) and functional MRI (fMRI) from normal healthy control data for a given age group. Using these pipelines, which were executed on a GPU-based high-performance computing platform, the data were strictly and carefully preprocessed. Next, scale- and shift-invariant low- to high-level features were obtained from a high volume of training images using convolutional neural network (CNN) architecture. In this study, fMRI data were used for the first time in deep learning applications for the purposes of medical image analysis and Alzheimer’s disease prediction. These proposed and implemented pipelines, which demonstrate a significant improvement in classification output over other studies, resulted in high and reproducible accuracy rates of 99.9% and 98.84% for the fMRI and MRI pipelines, respectively. Additionally, for clinical purposes, subject-level classification was performed, resulting in an average accuracy rate of 94.32% and 97.88% for the fMRI and MRI pipelines, respectively. Finally, a decision making algorithm designed for the subject-level classification improved the rate to 97.77% for fMRI and 100% for MRI pipelines.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/070441",
    "publisher": "bioRxiv",
    "title": "DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI",
    "URL": "https://doi.org/gcgk9b",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/070441"
  },
  {
    "type": "article-journal",
    "id": "zuLdSQx3",
    "author": [
      {
        "family": "Tan",
        "given": "Jie"
      },
      {
        "family": "Doing",
        "given": "Georgia"
      },
      {
        "family": "Lewis",
        "given": "Kimberley A."
      },
      {
        "family": "Price",
        "given": "Courtney E."
      },
      {
        "family": "Chen",
        "given": "Kathleen M."
      },
      {
        "family": "Cady",
        "given": "Kyle C."
      },
      {
        "family": "Perchuk",
        "given": "Barret"
      },
      {
        "family": "Laub",
        "given": "Michael T."
      },
      {
        "family": "Hogan",
        "given": "Deborah A."
      },
      {
        "family": "Greene",
        "given": "Casey S."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          4,
          10
        ]
      ]
    },
    "abstract": "Cross experiment comparisons in public data compendia are challenged by unmatched conditions and technical noise. The ADAGE method, which performs unsupervised integration with neural networks, can effectively identify biological patterns, but because ADAGE models, like many neural networks, are over-parameterized, different ADAGE models perform equally well. To enhance model robustness and better build signatures consistent with biological pathways, we developed an ensemble ADAGE (eADAGE) that integrated stable signatures across models. We applied eADAGE to a compendium containing experiments performed in 78 media. eADAGE revealed a phosphate starvation response controlled by PhoB. While we expected PhoB activity in limiting phosphate conditions, our analyses found PhoB activity in other media with moderate phosphate and predicted that a second stimulus provided by the sensor kinase, KinB, is required for PhoB activation in this setting. We validated this relationship using both targeted and unbiased genetic approaches. eADAGE, which captures stable biological patterns, enables cross-experiment comparisons that can highlight measured but undiscovered relationships.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/078659",
    "publisher": "bioRxiv",
    "title": "Unsupervised extraction of stable expression signatures from public compendia with eADAGE",
    "URL": "https://doi.org/gcgk9c",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/078659"
  },
  {
    "type": "article-journal",
    "id": "ECTm1SuA",
    "author": [
      {
        "family": "Torracinta",
        "given": "Remi"
      },
      {
        "family": "Mesnard",
        "given": "Laurent"
      },
      {
        "family": "Levine",
        "given": "Susan"
      },
      {
        "family": "Shaknovich",
        "given": "Rita"
      },
      {
        "family": "Hanson",
        "given": "Maureen"
      },
      {
        "family": "Levine",
        "given": "Susan"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          10,
          4
        ]
      ]
    },
    "abstract": "A number of approaches have been developed to call somatic variation in high-throughput sequencing data. Here, we present an adaptive approach to calling somatic variations. Our approach trains a deep feed-forward neural network with semi-simulated data. Semi-simulated datasets are constructed by planting somatic mutations in real datasets where no mutations are expected. Using semi-simulated data makes it possible to train the models with millions of training examples, a usual requirement for successfully training deep learning models. We initially focus on calling variations in RNA-Seq data. We derive semi-simulated datasets from real RNA-Seq data, which offer a good representation of the data the models will be applied to. We test the models on independent semi-simulated data as well as pure simulations. On independent semi-simulated data, models achieve an AUC of 0.973. When tested on semi-simulated exome DNA datasets, we find that the models trained on RNA-Seq data remain predictive (sens 0.4 &amp; spec 0.9 at cutoff of &gt; = 0.9), albeit with lower overall performance (AUC=0.737). Interestingly, while the models generalize across assay, training on RNA-Seq data lowers the confidence for a group of mutations. Haloplex exome specific training was also performed, demonstrating that the approach can produce probabilistic models tuned for specific assays and protocols. We found that the method adapts to the characteristics of experimental protocol. We further illustrate these points by training a model for a trio somatic experimental design when germline DNA of both parents is available in addition to data about the individual. These models are distributed with Goby ( ).",
    "container-title": "bioRxiv",
    "DOI": "10.1101/079087",
    "publisher": "bioRxiv",
    "title": "Adaptive Somatic Mutations Calls with Deep Learning and Semi-Simulated Data",
    "URL": "https://doi.org/gcgk9d",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/079087"
  },
  {
    "type": "article-journal",
    "id": "gllSeTW",
    "author": [
      {
        "family": "Eulenberg",
        "given": "Philipp"
      },
      {
        "family": "Köhler",
        "given": "Niklas"
      },
      {
        "family": "Blasi",
        "given": "Thomas"
      },
      {
        "family": "Filby",
        "given": "Andrew"
      },
      {
        "family": "Carpenter",
        "given": "Anne E."
      },
      {
        "family": "Rees",
        "given": "Paul"
      },
      {
        "family": "Theis",
        "given": "Fabian J."
      },
      {
        "family": "Wolf",
        "given": "F. Alexander"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          6,
          5
        ]
      ]
    },
    "abstract": "We show that deep convolutional neural networks combined with non-linear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by recon-structing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a 6-fold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/081364",
    "publisher": "bioRxiv",
    "title": "Reconstructing cell cycle and disease progression using deep learning",
    "URL": "https://doi.org/gcgk9f",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/081364"
  },
  {
    "type": "article-journal",
    "id": "yOz8Ybj2",
    "author": [
      {
        "family": "Eser",
        "given": "Umet"
      },
      {
        "family": "Churchman",
        "given": "L. Stirling"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          10,
          17
        ]
      ]
    },
    "abstract": "Numerous advances in sequencing technologies have revolutionized genomics through generating many types of genomic functional data. Statistical tools have been developed to analyze individual data types, but there lack strategies to integrate disparate datasets under a unified framework. Moreover, most analysis techniques heavily rely on feature selection and data preprocessing which increase the difficulty of addressing biological questions through the integration of multiple datasets. Here, we introduce FIDDLE (Flexible Integration of Data with Deep LEarning) an open source data-agnostic flexible integrative framework that learns a unified representation from multiple data types to infer another data type. As a case study, we use multiple genomic datasets to predict global transcription start sites (TSS) through the simulation of TSS-seq data. We demonstrate that a type of data can be inferred from other sources of data types without manually specifying the relevant features and preprocessing. We show that models built from multiple genome-wide datasets perform profoundly better than models built from individual datasets. Thus FIDDLE learns the complex synergistic relationship within individual datasets and, importantly, across datasets.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/081380",
    "publisher": "bioRxiv",
    "title": "FIDDLE: An integrative deep learning framework for functional genomic data inference",
    "URL": "https://doi.org/gcgk9g",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/081380"
  },
  {
    "type": "article-journal",
    "id": "14Ovc5nPg",
    "author": [
      {
        "family": "Leibig",
        "given": "Christian"
      },
      {
        "family": "Allken",
        "given": "Vaneeda"
      },
      {
        "family": "Ayhan",
        "given": "Murat Seçkin"
      },
      {
        "family": "Berens",
        "given": "Philipp"
      },
      {
        "family": "Wahl",
        "given": "Siegfried"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          10,
          18
        ]
      ]
    },
    "abstract": "Deep learning (DL) has revolutionized the field of computer vision and image processing. In medical imaging, algorithmic solutions based on DL have been shown to achieve high performance on tasks that previously required medical experts. However, DL-based solutions for disease detection have been proposed without methods to quantify and control their uncertainty in a decision. In contrast, a physician knows whether she is uncertain about a case and will consult more experienced colleagues if needed. Here we evaluate drop-out based Bayesian uncertainty measures for DL in diagnosing diabetic retinopathy (DR) from fundus images and show that it captures uncertainty better than straightforward alternatives. Furthermore, we show that uncertainty informed decision referral can improve diagnostic performance. Experiments across different networks, tasks and datasets show robust generalization. Depending on network capacity and task/dataset difficulty, we surpass 85% sensitivity and 80% specificity as recommended by the NHS when referring 0%–20% of the most uncertain decisions for further inspection. We analyse causes of uncertainty by relating intuitions from 2D visualizations to the high-dimensional image space. While uncertainty is sensitive to clinically relevant cases, sensitivity to unfamiliar data samples is task dependent, but can be rendered more robust.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/084210",
    "publisher": "bioRxiv",
    "title": "Leveraging uncertainty information from deep neural networks for disease detection",
    "URL": "https://doi.org/gcgk9h",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/084210"
  },
  {
    "type": "article-journal",
    "id": "BMg062hc",
    "author": [
      {
        "family": "Pawlowski",
        "given": "Nick"
      },
      {
        "family": "Caicedo",
        "given": "Juan C"
      },
      {
        "family": "Singh",
        "given": "Shantanu"
      },
      {
        "family": "Carpenter",
        "given": "Anne E"
      },
      {
        "family": "Storkey",
        "given": "Amos"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          11,
          2
        ]
      ]
    },
    "abstract": "Morphological profiling aims to create signatures of genes, chemicals and diseases from microscopy images. Current approaches use classical computer vision-based segmentation and feature extraction. Deep learning models achieve state-of-the-art performance in many computer vision tasks such as classification and segmentation. We propose to transfer activation features of generic deep convolutional networks to extract features for morphological profiling. Our approach surpasses currently used methods in terms of accuracy and processing speed. Furthermore, it enables fully automated processing of microscopy images without need for single cell identification.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/085118",
    "publisher": "bioRxiv",
    "title": "Automating Morphological Profiling with Generic Deep Convolutional Networks",
    "URL": "https://doi.org/gcgk9j",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/085118"
  },
  {
    "type": "article-journal",
    "id": "14TqLB9iZ",
    "author": [
      {
        "family": "Singh",
        "given": "Shashank"
      },
      {
        "family": "Yang",
        "given": "Yang"
      },
      {
        "family": "Póczos",
        "given": "Barnabás"
      },
      {
        "family": "Ma",
        "given": "Jian"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          2,
          5
        ]
      ]
    },
    "abstract": "In the human genome, distal enhancers are involved in regulating target genes through proxi-mal promoters by forming enhancer-promoter interactions. Although recently developed high-throughput experimental approaches have allowed us to recognize potential enhancer-promoter interactions genome-wide, it is still largely unclear to what extent the sequence-level information encoded in our genome help guide such interactions. Here we report a new computational method (named “SPEID”) using deep learning models to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given. Our results across six different cell types demonstrate that SPEID is effective in predicting enhancer-promoter interactions as compared to state-of-the-art methods that only use information from a single cell type. As a proof-of-principle, we also applied SPEID to identify somatic non-coding mutations in melanoma samples that may have reduced enhancer-promoter interactions in tumor genomes. This work demonstrates that deep learning models can help reveal that sequence-based features alone are sufficient to reliably predict enhancer-promoter interactions genome-wide.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/085241",
    "publisher": "bioRxiv",
    "title": "Predicting Enhancer-Promoter Interaction from Genomic Sequence with Deep Neural Networks",
    "URL": "https://doi.org/gcgk9k",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/085241"
  },
  {
    "type": "article-journal",
    "id": "FVfZESYP",
    "author": [
      {
        "family": "Poplin",
        "given": "Ryan"
      },
      {
        "family": "Chang",
        "given": "Pi-Chuan"
      },
      {
        "family": "Alexander",
        "given": "David"
      },
      {
        "family": "Schwartz",
        "given": "Scott"
      },
      {
        "family": "Colthurst",
        "given": "Thomas"
      },
      {
        "family": "Ku",
        "given": "Alexander"
      },
      {
        "family": "Newburger",
        "given": "Dan"
      },
      {
        "family": "Dijamco",
        "given": "Jojo"
      },
      {
        "family": "Nguyen",
        "given": "Nam"
      },
      {
        "family": "Afshar",
        "given": "Pegah T."
      },
      {
        "family": "Gross",
        "given": "Sam S."
      },
      {
        "family": "Dorfman",
        "given": "Lizzie"
      },
      {
        "family": "McLean",
        "given": "Cory Y."
      },
      {
        "family": "DePristo",
        "given": "Mark A."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          3,
          20
        ]
      ]
    },
    "abstract": "Next-generation sequencing (NGS) is a rapidly evolving set of technologies that can be used to determine the sequence of an individual’s genome by calling genetic variants present in an individual using billions of short, errorful sequence reads . Despite more than a decade of effort and thousands of dedicated researchers, the hand-crafted and parameterized statistical models used for variant calling still produce thousands of errors and missed variants in each genome . Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships (likelihoods) between images of read pileups around putative variant sites and ground-truth genotype calls. This approach, called DeepVariant, outperforms existing tools, even winning the “highest performance” award for SNPs in a FDA-administered variant calling challenge. The learned model generalizes across genome builds and even to other mammalian species, allowing non-human sequencing projects to benefit from the wealth of human ground truth data. We further show that, unlike existing tools which perform well on only a specific technology, DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, from deep whole genomes from 10X Genomics to Ion Ampliseq exomes. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/092890",
    "publisher": "bioRxiv",
    "title": "Creating a universal SNP and small indel variant caller with deep neural networks",
    "URL": "https://doi.org/gcgk9m",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/092890"
  },
  {
    "type": "article-journal",
    "id": "jXjVdzvo",
    "author": [
      {
        "family": "Campagne",
        "given": "Fabien"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          12,
          13
        ]
      ]
    },
    "abstract": "In , we presented adaptive models for calling somatic mutations in high-throughput sequencing data. These models were developed by training deep neural networks with semi-simulated data. In this continuation, I evaluate how such models can predict known somatic mutations in a real dataset. To address this question, I tested the approach using samples from the International Cancer Genome Consortium (ICGC) and the previously published ground-truth mutations (GoldSet). This evaluation revealed that training models with semi-simulation does produce models that exhibit strong performance in real datasets. I found a linear relationship between the performance observed on a semi-simulated validation set and independent ground-truth in the gold set ( = 0.952, &lt; 2 ). I also found that semi-simulation can be used to pre-train models before continuing training with true labels and that this pre-training improves model performance substantially on the real dataset compared to training models only with the real dataset. The best model pre-trained with semi-simulation achieved an AUC of 0.969 [0.957-0.982] (95% confidence interval) compared to 0.911 [0.890-0.932] when training with real labels only. These data demonstrate that semi-simulation can be a very effective approach to training filtering and ranking probabilistic models.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/093534",
    "publisher": "bioRxiv",
    "title": "CONTINUATION: Evaluation of adaptive somatic models in a gold standard whole genome somatic dataset",
    "URL": "https://doi.org/gf5gxz",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/093534"
  },
  {
    "type": "article-journal",
    "id": "SxsZyrVM",
    "author": [
      {
        "family": "Lee",
        "given": "Cecilia S"
      },
      {
        "family": "Baughman",
        "given": "Doug M"
      },
      {
        "family": "Lee",
        "given": "Aaron Y"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          12,
          14
        ]
      ]
    },
    "abstract": "Objective: The advent of Electronic Medical Records (EMR) with large electronic imaging databases along with advances in deep neural networks with machine learning has provided a unique opportunity to achieve milestones in automated image analysis. Optical coherence tomography (OCT) is the most commonly obtained imaging modality in ophthalmology and represents a dense and rich dataset when combined with labels derived from the EMR. We sought to determine if deep learning could be utilized to distinguish normal OCT images from images from patients with Age-related Macular Degeneration (AMD). Design: EMR and OCT database study Subjects: Normal and AMD patients who had a macular OCT. Methods: Automated extraction of an OCT imaging database was performed and linked to clinical endpoints from the EMR. OCT macula scans were obtained by Heidelberg Spectralis, and each OCT scan was linked to EMR clinical endpoints extracted from EPIC. The central 11 images were selected from each OCT scan of two cohorts of patients: normal and AMD. Cross-validation was performed using a random subset of patients. Receiver operator curves (ROC) were constructed at an independent image level, macular OCT level, and patient level. Main outcome measure: Area under the ROC. Results: Of a recent extraction of 2.6 million OCT images linked to clinical datapoints from the EMR, 52,690 normal macular OCT images and 48,312 AMD macular OCT images were selected. A deep neural network was trained to categorize images as either normal or AMD. At the image level, we achieved an area under the ROC of 92.78% with an accuracy of 87.63%. At the macula level, we achieved an area under the ROC of 93.83% with an accuracy of 88.98%. At a patient level, we achieved an area under the ROC of 97.45% with an accuracy of 93.45%. Peak sensitivity and specificity with optimal cutoffs were 92.64% and 93.69% respectively. Conclusions: Deep learning techniques achieve high accuracy and is effective as a new image classification technique. These findings have important implications in utilizing OCT in automated screening and the development of computer aided diagnosis tools in the future.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/094276",
    "publisher": "bioRxiv",
    "title": "Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration",
    "URL": "https://doi.org/gcgk9n",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/094276"
  },
  {
    "type": "article-journal",
    "id": "mo3GQwJj",
    "author": [
      {
        "family": "Artemov",
        "given": "Artem V."
      },
      {
        "family": "Putin",
        "given": "Evgeny"
      },
      {
        "family": "Vanhaelen",
        "given": "Quentin"
      },
      {
        "family": "Aliper",
        "given": "Alexander"
      },
      {
        "family": "Ozerov",
        "given": "Ivan V."
      },
      {
        "family": "Zhavoronkov",
        "given": "Alex"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          12,
          29
        ]
      ]
    },
    "abstract": "Despite many recent advances in systems biology and a marked increase in the availability of high-throughput biological data, the productivity of research and development in the pharmaceutical industry is on the decline. This is primarily due to clinical trial failure rates reaching up to 95% in oncology and other disease areas. We have developed a comprehensive analytical and computational pipeline utilizing deep learning techniques and novel systems biology analytical tools to predict the outcomes of phase I/II clinical trials. The pipeline predicts the side effects of a drug using deep neural networks and estimates drug-induced pathway activation. It then uses the predicted side effect probabilities and pathway activation scores as an input to train a classifier which predicts clinical trial outcomes. This classifier was trained on 577 transcriptomic datasets and has achieved a cross-validated accuracy of 0.83. When compared to a direct gene-based classifier, our multi-stage approach dramatically improves the accuracy of the predictions. The classifier was applied to a set of compounds currently present in the pipelines of several major pharmaceutical companies to highlight potential risks in their portfolios and estimate the fraction of clinical trials that were likely to fail in phase I and II.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/095653",
    "publisher": "bioRxiv",
    "title": "Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes",
    "URL": "https://doi.org/gcgk9p",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/095653"
  },
  {
    "type": "article-journal",
    "id": "Xxb4t3zO",
    "author": [
      {
        "family": "Zhu",
        "given": "Wentao"
      },
      {
        "family": "Xie",
        "given": "Xiaohui"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          12,
          20
        ]
      ]
    },
    "abstract": "Mass segmentation is an important task in mammogram analysis, providing effective morphological features and regions of interest (ROI) for mass detection and classification. Inspired by the success of using deep convolutional features for natural image analysis and conditional random fields (CRF) for structural learning, we propose an end-to-end network for mammographic mass segmentation. The network employs a fully convolutional network (FCN) to model potential function, followed by a CRF to perform structural learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with position priori for the task. Due to the small size of mammogram datasets, we use adversarial training to control over-fitting. Four models with different convolutional kernels are further fused to improve the segmentation results. Experimental results on two public datasets, INbreast and DDSM-BCRP, show that our end-to-end network combined with adversarial training achieves the-state-of-the-art results.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/095786",
    "publisher": "bioRxiv",
    "title": "Adversarial Deep Structural Networks for Mammographic Mass Segmentation",
    "URL": "https://doi.org/gcgk9q",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/095786"
  },
  {
    "type": "article-journal",
    "id": "9G9Hv1Pp",
    "author": [
      {
        "family": "Zhu",
        "given": "Wentao"
      },
      {
        "family": "Lou",
        "given": "Qi"
      },
      {
        "family": "Vang",
        "given": "Yeeleng Scott"
      },
      {
        "family": "Xie",
        "given": "Xiaohui"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          12,
          20
        ]
      ]
    },
    "abstract": "Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multiinstance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed deep networks compared to previous work using segmentation and detection annotations in the training.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/095794",
    "publisher": "bioRxiv",
    "title": "Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification",
    "URL": "https://doi.org/gcgk9r",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/095794"
  },
  {
    "type": "article-journal",
    "id": "GSLRw2L5",
    "author": [
      {
        "family": "Torracinta",
        "given": "Remi"
      },
      {
        "family": "Campagne",
        "given": "Fabien"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          12,
          30
        ]
      ]
    },
    "abstract": "We present an open source software toolkit for training deep learning models to call genotypes in high-throughput sequencing data. The software supports SAM, BAM, CRAM and Goby alignments and the training of models for a variety of experimental assays and analysis protocols. We evaluate this software in the Illumina Platinum whole genome datasets and find that a deep learning model trained on 80% of the genome achieves a 0.986% accuracy on variants (genotype concordance) when trained with 10% of the data from a genome. The software is distributed at . The software makes it possible to train genotype calling models on consumer hardware with CPUs or GPU(s). It will enable individual investigators and small laboratories to train and evaluate their own models and to make open source contributions. We welcome contributions to extend this early prototype or evaluate its performance on other gold standard datasets.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/097469",
    "publisher": "bioRxiv",
    "title": "Training Genotype Callers with Neural Networks",
    "URL": "https://doi.org/gcgk9s",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/097469"
  },
  {
    "type": "article-journal",
    "id": "iEmvzeT8",
    "author": [
      {
        "family": "Shrikumar",
        "given": "Avanti"
      },
      {
        "family": "Greenside",
        "given": "Peyton"
      },
      {
        "family": "Kundaje",
        "given": "Anshul"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          1,
          27
        ]
      ]
    },
    "abstract": "Deep learning approaches that have produced breakthrough predictive models in computer vision, speech recognition and machine translation are now being successfully applied to problems in regulatory genomics. However, deep learning architectures used thus far in genomics are often directly ported from computer vision and natural language processing applications with few, if any, domain-specific modifications. In double-stranded DNA, the same pattern may appear identically on one strand and its reverse complement due to complementary base pairing. Here, we show that conventional deep learning models that do not explicitly model this property can produce substantially different predictions on forward and reverse-complement versions of the same DNA sequence. We present four new convolutional neural network layers that leverage the reverse-complement property of genomic DNA sequence by sharing parameters between forward and reverse-complement representations in the model. These layers guarantee that forward and reverse-complement sequences produce identical predictions within numerical precision. Using experiments on simulated and in vivo transcription factor binding data, we show that our proposed architectures lead to improved performance, faster learning and cleaner internal representations compared to conventional architectures trained on the same data.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/103663",
    "publisher": "bioRxiv",
    "title": "Reverse-complement parameter sharing improves deep learning models for genomics",
    "URL": "https://doi.org/gcgk9t",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/103663"
  },
  {
    "type": "article-journal",
    "id": "N0HBi8MH",
    "author": [
      {
        "family": "Jha",
        "given": "Anupama"
      },
      {
        "family": "Gazzara",
        "given": "Matthew R."
      },
      {
        "family": "Barash",
        "given": "Yoseph"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          1,
          31
        ]
      ]
    },
    "container-title": "bioRxiv",
    "DOI": "10.1101/104869",
    "publisher": "bioRxiv",
    "title": "Integrative Deep Models for Alternative Splicing",
    "URL": "https://doi.org/gcgk9v",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/104869"
  },
  {
    "type": "article-journal",
    "id": "VjsZbMSz",
    "author": [
      {
        "family": "Finnegan",
        "given": "Alex"
      },
      {
        "family": "Song",
        "given": "Jun S."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          10,
          5
        ]
      ]
    },
    "abstract": "New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/105957",
    "publisher": "bioRxiv",
    "title": "Maximum entropy methods for extracting the learned features of deep neural networks",
    "URL": "https://doi.org/gcgk9w",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/105957"
  },
  {
    "type": "article-journal",
    "id": "Oljj2W96",
    "author": [
      {
        "family": "Qiu",
        "given": "Xiaojie"
      },
      {
        "family": "Mao",
        "given": "Qi"
      },
      {
        "family": "Tang",
        "given": "Ying"
      },
      {
        "family": "Wang",
        "given": "Li"
      },
      {
        "family": "Chawla",
        "given": "Raghav"
      },
      {
        "family": "Pliner",
        "given": "Hannah"
      },
      {
        "family": "Trapnell",
        "given": "Cole"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          2,
          21
        ]
      ]
    },
    "abstract": "Organizing single cells along a developmental trajectory has emerged as a powerful tool for understanding how gene regulation governs cell fate decisions. However, learning the structure of complex single-cell trajectories with two or more branches remains a challenging computational problem. We present Monocle 2, which uses reversed graph embedding to reconstruct single-cell trajectories in a fully unsupervised manner. Monocle 2 learns an explicit principal graph to describe the data, greatly improving the robustness and accuracy of its trajectories compared to other algorithms. Monocle 2 uncovered a new, alternative cell fate in what we previously reported to be a linear trajectory for differentiating myoblasts. We also reconstruct branched trajectories for two studies of blood development, and show that loss of function mutations in key lineage transcription factors diverts cells to alternative branches on the a trajectory. Monocle 2 is thus a powerful tool for analyzing cell fate decisions with single-cell genomics.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/110668",
    "publisher": "bioRxiv",
    "title": "Reversed graph embedding resolves complex single-cell developmental trajectories",
    "URL": "https://doi.org/gcgk9x",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/110668"
  },
  {
    "type": "article-journal",
    "id": "obeRVckH",
    "author": [
      {
        "family": "Chaudhary",
        "given": "Kumardeep"
      },
      {
        "family": "Poirion",
        "given": "Olivier B."
      },
      {
        "family": "Lu",
        "given": "Liangqun"
      },
      {
        "family": "Garmire",
        "given": "Lana X."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          9,
          18
        ]
      ]
    },
    "abstract": "Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill in this gap, we present a deep learning (DL) based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts. We build the DL based, survival-sensitive model on 360 HCC patients’ data using RNA-seq, miRNA-seq and methylation data from TCGA, which predicts prognosis as good as an alternative model where genomics and clinical data are both considered. This DL based model provides two optimal subgroups of patients with significant survival differences (P=7.13e-6) and good model fitness (C-index=0.68). More aggressive subtype is associated with frequent inactivation mutations, higher expression of stemness markers ( , ) and tumor marker , and activated Wnt and Akt signaling pathways. We validated this multi-omics model on five external datasets of various omics types: LIRI-JP cohort (n=230, C-index=0.75), NCI cohort (n=221, C-index=0.67), Chinese cohort (n=166, C-index=0.69), E-TABM-36 cohort (n=40, C-index=0.77), and Hawaiian cohort (n=27, C-index=0.82). This is the first study to employ deep learning to identify multi-omics features linked to the differential survival of HCC patients. Given its robustness over multiple cohorts, we expect this workflow to be useful at predicting HCC prognosis prediction.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/114892",
    "publisher": "bioRxiv",
    "title": "Deep Learning based multi-omics integration robustly predicts survival in liver cancer",
    "URL": "https://doi.org/gcgk9z",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/114892"
  },
  {
    "type": "article-journal",
    "id": "2M3zXijc",
    "author": [
      {
        "family": "Shao",
        "given": "Mingfu"
      },
      {
        "family": "Ma",
        "given": "Jianzhu"
      },
      {
        "family": "Wang",
        "given": "Sheng"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          4,
          7
        ]
      ]
    },
    "container-title": "bioRxiv",
    "DOI": "10.1101/125229",
    "publisher": "bioRxiv",
    "title": "DeepBound: Accurate Identification of Transcript Boundaries via Deep Convolutional Neural Fields",
    "URL": "https://doi.org/gcgk92",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/125229"
  },
  {
    "type": "article-journal",
    "id": "Exe9wdYF",
    "author": [
      {
        "family": "Kietzmann",
        "given": "Tim C"
      },
      {
        "family": "McClure",
        "given": "Patrick"
      },
      {
        "family": "Kriegeskorte",
        "given": "Nikolaus"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          6,
          5
        ]
      ]
    },
    "abstract": "The goal of computational neuroscience is to find mechanistic explanations of how the nervous system processes information to give rise to cognitive function and behaviour. At the heart of the field are its models, i.e. mathematical and computational descriptions of the system being studied, which map sensory stimuli to neural responses and/or neural to behavioural responses. These models range from simple to complex. Recently, deep neural networks (DNNs) have come to dominate several domains of artificial intelligence (AI). As the term “neural network” suggests, these models are inspired by biological brains. However, current DNNs neglect many details of biological neural networks. These simplifications contribute to their computational efficiency, enabling them to perform complex feats of intelligence, ranging from perceptual (e.g. visual object and auditory speech recognition) to cognitive tasks (e.g. machine translation), and on to motor control (e.g. playing computer games or controlling a robot arm). In addition to their ability to model complex intelligent behaviours, DNNs excel at predicting neural responses to novel sensory stimuli with accuracies well beyond any other currently available model type. DNNs can have millions of parameters, which are required to capture the domain knowledge needed for successful task performance. Contrary to the intuition that this renders them into impenetrable black boxes, the computational properties of the network units are the result of four directly manipulable elements: , and . With full access to the activity and connectivity of all units, advanced visualization techniques, and analytic tools to map network representations to neural data, DNNs represent a powerful framework for building task-performing models and will drive substantial insights in computational neuroscience.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/133504",
    "publisher": "bioRxiv",
    "title": "Deep Neural Networks in Computational Neuroscience",
    "URL": "https://doi.org/gcsgrx",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/133504"
  },
  {
    "type": "article-journal",
    "id": "1CAw3FaPI",
    "author": [
      {
        "family": "Clark",
        "given": "Stephen J."
      },
      {
        "family": "Argelaguet",
        "given": "Ricard"
      },
      {
        "family": "Kapourani",
        "given": "Chantriolnt-Andreas"
      },
      {
        "family": "Stubbs",
        "given": "Thomas M."
      },
      {
        "family": "Lee",
        "given": "Heather J."
      },
      {
        "family": "Alda-Catalinas",
        "given": "Celia"
      },
      {
        "family": "Krueger",
        "given": "Felix"
      },
      {
        "family": "Sanguinetti",
        "given": "Guido"
      },
      {
        "family": "Kelsey",
        "given": "Gavin"
      },
      {
        "family": "Marioni",
        "given": "John C."
      },
      {
        "family": "Stegle",
        "given": "Oliver"
      },
      {
        "family": "Reik",
        "given": "Wolf"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          1,
          17
        ]
      ]
    },
    "abstract": "Parallel single-cell sequencing protocols represent powerful methods for investigating regulatory relationships, including epigenome-transcriptome interactions. Here, we report a novel single-cell method for parallel chromatin accessibility, DNA methylation and transcriptome profiling. scNMT-seq (single-cell nucleosome, methylation and transcription sequencing) uses a GpC methyltransferase to label open chromatin followed by bisulfite and RNA sequencing. We validate scNMT-seq by applying it to differentiating mouse embryonic stem cells, finding links between all three molecular layers and revealing dynamic coupling between epigenomic layers during differentiation.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/138685",
    "publisher": "bioRxiv",
    "title": "scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells",
    "URL": "https://doi.org/gcgk93",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/138685"
  },
  {
    "type": "article-journal",
    "id": "Zo0D80FN",
    "author": [
      {
        "family": "Alexandari",
        "given": "Amr Mohamed"
      },
      {
        "family": "Shrikumar",
        "given": "Avanti"
      },
      {
        "family": "Kundaje",
        "given": "Anshul"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          7,
          7
        ]
      ]
    },
    "abstract": "Convolutional neural networks are rapidly gaining popularity in regulatory genomics. Typically, these networks have a stack of convolutional and pooling layers, followed by one or more fully connected layers. In genomics, the same positional patterns are often present across multiple convolutional channels. Therefore, in current state-of-the-art networks, there exists significant redundancy in the representations learned by standard fully connected layers. We present a new separable fully connected layer that learns a weights tensor that is the outer product of positional weights and cross-channel weights, thereby allowing the same positional patterns to be applied across multiple convolutional channels. Decomposing positional and cross-channel weights further enables us to readily impose biologically-inspired constraints on positional weights, such as symmetry. We also propose a novel regularizer and constraint that act on curvature in the positional weights. Using experiments on simulated and datasets, we show that networks that incorporate our separable fully connected layer outperform conventional models with analogous architectures and the same number of parameters. Additionally, our networks are more robust to hyperparameter tuning, have more informative gradients, and produce importance scores that are more consistent with known biology than conventional deep neural networks.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/146431",
    "publisher": "bioRxiv",
    "title": "Separable Fully Connected Layers Improve Deep Learning Models For Genomics",
    "URL": "https://doi.org/gcsmgh",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/146431"
  },
  {
    "type": "article-journal",
    "id": "BguHMHkG",
    "author": [
      {
        "family": "Quang",
        "given": "Daniel"
      },
      {
        "family": "Xie",
        "given": "Xiaohui"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          6,
          28
        ]
      ]
    },
    "abstract": "Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all TF/cell type pairs is not experimentally feasible, owing to constraints in time and resources. To address this issue, we developed a convolutional-recurrent neural network model, called FactorNet, to computationally impute the missing binding data. FactorNet trains on binding data from reference cell types to make accurate predictions on testing cell types by leveraging a variety of features, including genomic sequences, genome annotations, gene expression, and single-nucleotide resolution sequential signals, such as DNase I cleavage. To the best of our knowledge, this is the first deep learning method to study the rules governing TF binding at such a fine resolution. With FactorNet, a researcher can perform a single sequencing assay, such as DNase-seq, on a cell type and computationally impute dozens of TF binding profiles. This is an integral step for reconstructing the complex networks underlying gene regulation. While neural networks can be computationally expensive to train, we introduce several novel strategies to significantly reduce the overhead. By visualizing the neural network models, we can interpret how the model predicts binding which in turn reveals additional insights into regulatory grammar. We also investigate the variables that affect cross-cell type predictive performance to explain why the model performs better on some TF/cell types than others, and offer insights to improve upon this field. Our method ranked among the top four teams in the ENCODE-DREAM Transcription Factor Binding Site Prediction Challenge.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/151274",
    "publisher": "bioRxiv",
    "title": "FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data",
    "URL": "https://doi.org/gcpvb3",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/151274"
  },
  {
    "type": "article-journal",
    "id": "1E5vZzUF2",
    "author": [
      {
        "family": "Pandarinath",
        "given": "Chethan"
      },
      {
        "family": "O’Shea",
        "given": "Daniel J."
      },
      {
        "family": "Collins",
        "given": "Jasmine"
      },
      {
        "family": "Jozefowicz",
        "given": "Rafal"
      },
      {
        "family": "Stavisky",
        "given": "Sergey D."
      },
      {
        "family": "Kao",
        "given": "Jonathan C."
      },
      {
        "family": "Trautmann",
        "given": "Eric M."
      },
      {
        "family": "Kaufman",
        "given": "Matthew T."
      },
      {
        "family": "Ryu",
        "given": "Stephen I."
      },
      {
        "family": "Hochberg",
        "given": "Leigh R."
      },
      {
        "family": "Henderson",
        "given": "Jaimie M."
      },
      {
        "family": "Shenoy",
        "given": "Krishna V."
      },
      {
        "family": "Abbott",
        "given": "L. F."
      },
      {
        "family": "Sussillo",
        "given": "David"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          6,
          20
        ]
      ]
    },
    "abstract": "Neuroscience is experiencing a data revolution in which simultaneous recording of many hundreds or thousands of neurons is revealing structure in population activity that is not apparent from single-neuron responses. This structure is typically extracted from trial-averaged data. Single-trial analyses are challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. Here we introduce Latent Factor Analysis via Dynamical Systems (LFADS), a deep learning method to infer latent dynamics from single-trial neural spiking data. LFADS uses a nonlinear dynamical system (a recurrent neural network) to infer the dynamics underlying observed population activity and to extract ‘de-noised’ single-trial firing rates from neural spiking data. We apply LFADS to a variety of monkey and human motor cortical datasets, demonstrating its ability to predict observed behavioral variables with unprecedented accuracy, extract precise estimates of neural dynamics on single trials, infer perturbations to those dynamics that correlate with behavioral choices, and combine data from non-overlapping recording sessions (spanning months) to improve inference of underlying dynamics. In summary, LFADS leverages all observations of a neural population’s activity to accurately model its dynamics on single trials, opening the door to a detailed understanding of the role of dynamics in performing computation and ultimately driving behavior.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/152884",
    "publisher": "bioRxiv",
    "title": "Inferring single-trial neural population dynamics using sequential auto-encoders",
    "URL": "https://doi.org/gcsgrz",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/152884"
  },
  {
    "type": "article-journal",
    "id": "1aswoG70",
    "author": [
      {
        "family": "Bhattacharya",
        "given": "Rohit"
      },
      {
        "family": "Sivakumar",
        "given": "Ashok"
      },
      {
        "family": "Tokheim",
        "given": "Collin"
      },
      {
        "family": "Guthrie",
        "given": "Violeta Beleva"
      },
      {
        "family": "Anagnostou",
        "given": "Valsamo"
      },
      {
        "family": "Velculescu",
        "given": "Victor E."
      },
      {
        "family": "Karchin",
        "given": "Rachel"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          7,
          27
        ]
      ]
    },
    "abstract": "Binding of peptides to Major Histocompatibility Complex (MHC) proteins is a critical step in immune response. Peptides bound to MHCs are recognized by CD8+ (MHC Class I) and CD4+ (MHC Class II) T-cells. Successful prediction of which peptides will bind to specific MHC alleles would benefit many cancer immunotherapy appications. Currently, supervised machine learning is the leading computational approach to predict peptide-MHC binding, and a number of methods, trained using results of binding assays, have been published. Many clinical researchers are dissatisfied with the sensitivity and specificity of currently available methods and the limited number of alleles for which they can be applied. We evaluated several recent methods to predict peptide-MHC Class I binding affinities and a new method of our own design (MHCnuggets). We used a high-quality benchmark set of 51 alleles, which has been applied previously. The neural network methods NetMHC, NetMHCpan, MHCflurry, and MHCnuggets achieved similar best-in-class prediction performance in our testing, and of these methods MHCnuggets was significantly faster. MHCnuggets is a gated recurrent neural network, and the only method to our knowledge which can handle peptides of any length, without artificial lengthening and shortening. Seventeen alleles were problematic for all tested methods. Prediction difficulties could be explained by deficiencies in the training and testing examples in the benchmark, suggesting that biological differences in allele-specific binding properties are not as important as previously claimed. Advances in accuracy and speed of computational methods to predict peptide-MHC affinity are urgently needed. These methods will be at the core of pipelines to identify patients who will benefit from immunotherapy, based on tumor-derived somatic mutations. Machine learning methods, such as MHCnuggets, which efficiently handle peptides of any length will be increasingly important for the challenges of predicting immunogenic response for MHC Class II alleles.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/154757",
    "publisher": "bioRxiv",
    "title": "Evaluation of machine learning methods to predict peptide binding to MHC Class I proteins",
    "URL": "https://doi.org/gcpzg5",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/154757"
  },
  {
    "type": "article-journal",
    "id": "fbIH12yd",
    "author": [
      {
        "family": "Beaulieu-Jones",
        "given": "Brett K."
      },
      {
        "family": "Wu",
        "given": "Zhiwei Steven"
      },
      {
        "family": "Williams",
        "given": "Chris"
      },
      {
        "family": "Lee",
        "given": "Ran"
      },
      {
        "family": "Bhavnani",
        "given": "Sanjeev P."
      },
      {
        "family": "Byrd",
        "given": "James Brian"
      },
      {
        "family": "Greene",
        "given": "Casey S."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          12,
          20
        ]
      ]
    },
    "container-title": "bioRxiv",
    "DOI": "10.1101/159756",
    "publisher": "bioRxiv",
    "title": "Privacy-preserving generative deep neural networks support clinical data sharing",
    "URL": "https://doi.org/gcnzrn",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/159756"
  },
  {
    "type": "article-journal",
    "id": "9ZNxZTxD",
    "author": [
      {
        "family": "O’Donnell",
        "given": "Timothy"
      },
      {
        "family": "Rubinsteyn",
        "given": "Alex"
      },
      {
        "family": "Bonsack",
        "given": "Maria"
      },
      {
        "family": "Riemer",
        "given": "Angelika"
      },
      {
        "family": "Hammerbacher",
        "given": "Jeff"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          8,
          9
        ]
      ]
    },
    "abstract": "Machine learning prediction of the interaction between major histocompatibility complex I (MHC I) proteins and their small peptide ligands is important for vaccine design and other applications in adaptive immunity. We describe and benchmark a new open-source MHC I binding prediction package, MHCflurry. The software is a collection of allele-specific binding predictors incorporating a novel neural network architecture and adhering to software development best practices. MHCflurry outperformed the standard predictors NetMHC 4.0 and NetMHCpan 3.0 on a benchmark of mass spec-identified MHC ligands and showed competitive accuracy on a benchmark of affinity measurements. The accuracy improvement was due to substantially better prediction of non-9-mer peptide ligands, which offset a narrowly lower accuracy on 9-mers. MHCflurry was on average 8.6X faster than NetMHC and 44X faster than NetMHCpan; performance is further increased when a graphics processing unit (GPU) is available. MHCflurry is freely available to use, retrain, or extend, includes Python library and command line interfaces, and may be installed using standard package managers.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/174243",
    "publisher": "bioRxiv",
    "title": "MHCflurry: open-source class I MHC binding affinity prediction",
    "URL": "https://doi.org/gcpzg6",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/174243"
  },
  {
    "type": "article-journal",
    "id": "aWn0df1m",
    "author": [
      {
        "family": "Way",
        "given": "Gregory P."
      },
      {
        "family": "Greene",
        "given": "Casey S."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          10,
          2
        ]
      ]
    },
    "abstract": "The Cancer Genome Atlas (TCGA) has profiled over 10,000 tumors across 33 different cancer-types for many genomic features, including gene expression levels. Gene expression measurements capture substantial information about the state of each tumor. Certain classes of deep neural network models are capable of learning a meaningful latent space. Such a latent space could be used to explore and generate hypothetical gene expression profiles under various types of molecular and genetic perturbation. For example, one might wish to use such a model to predict a tumor’s response to specific therapies or to characterize complex gene expression activations existing in differential proportions in different tumors. Variational autoencoders (VAEs) are a deep neural network approach capable of generating meaningful latent spaces for image and text data. In this work, we sought to determine the extent to which a VAE can be trained to model cancer gene expression, and whether or not such a VAE would capture biologically-relevant features. In the following report, we introduce a VAE trained on TCGA pan-cancer RNA-seq data, identify specific patterns in the VAE encoded features, and discuss potential merits of the approach. We name our method “Tybalt” after an instigative, cat-like character who sets a cascading chain of events in motion in Shakespeare’s “ ”. From a systems biology perspective, Tybalt could one day aid in cancer stratification or predict specific activated expression patterns that would result from genetic changes or treatment effects.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/174474",
    "publisher": "bioRxiv",
    "title": "Extracting a Biologically Relevant Latent Space from Cancer Transcriptomes with Variational Autoencoders",
    "URL": "https://doi.org/gcm44d",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/174474"
  },
  {
    "type": "article-journal",
    "id": "yJxCo4h1",
    "author": [
      {
        "family": "Ding",
        "given": "Jiarui"
      },
      {
        "family": "Condon",
        "given": "Anne"
      },
      {
        "family": "Shah",
        "given": "Sohrab P."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          9,
          1
        ]
      ]
    },
    "abstract": "Single-cell RNA-sequencing has great potential to discover cell types, identify cell states, trace development lineages, and reconstruct the spatial organization of cells. However, dimension reduction to interpret structure in single-cell sequencing data remains a challenge. Existing algorithms are either not able to uncover the clustering structures in the data, or lose global information such as groups of clusters that are close to each other. We present a robust statistical model, scvis, to capture and visualize the low-dimensional structures in single-cell gene expression data. Simulation results demonstrate that low-dimensional representations learned by scvis preserve both the local and global neighbour structures in the data. In addition, scvis is robust to the number of data points and learns a probabilistic parametric mapping function to add new data points to an existing embedding. We then use scvis to analyze four single-cell RNA-sequencing datasets, exemplifying interpretable two-dimensional representations of the high-dimensional single-cell RNA-sequencing data.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/178624",
    "publisher": "bioRxiv",
    "title": "Interpretable dimensionality reduction of single cell transcriptome data with deep generative models",
    "URL": "https://doi.org/gcnzb8",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/178624"
  },
  {
    "type": "article-journal",
    "id": "1GOS0CRta",
    "author": [
      {
        "family": "Chen",
        "given": "Dexiong"
      },
      {
        "family": "Jacob",
        "given": "Laurent"
      },
      {
        "family": "Mairal",
        "given": "Julien"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2019,
          1,
          29
        ]
      ]
    },
    "abstract": "The growing number of annotated biological sequences available makes it possible to learn genotype-phenotype relationships from data with increasingly high accuracy. When large quantities of labeled samples are available for training a model, convolutional neural networks can be used to predict the phenotype of unannotated sequences with good accuracy. Unfortunately, their performance with medium- or small-scale datasets is mitigated, which requires inventing new data-efficient approaches. In this paper, we introduce a hybrid approach between convolutional neural networks and kernel methods to model biological sequences. Our method enjoys the ability of convolutional neural networks to learn data representations that are adapted to a specific task, while the kernel point of view yields algorithms that perform significantly better when the amount of training data is small. We illustrate these advantages for transcription factor binding prediction and protein homology detection, and we demonstrate that our model is also simple to interpret, which is crucial for discovering predictive motifs in sequences. The source code is freely available at .",
    "container-title": "bioRxiv",
    "DOI": "10.1101/217257",
    "publisher": "bioRxiv",
    "title": "Biological Sequence Modeling with Convolutional Kernel Networks",
    "URL": "https://doi.org/gcsmgj",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/217257"
  },
  {
    "type": "article-journal",
    "id": "dkPu3iv1",
    "author": [
      {
        "family": "Gligorijević",
        "given": "Vladimir"
      },
      {
        "family": "Barot",
        "given": "Meet"
      },
      {
        "family": "Bonneau",
        "given": "Richard"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          11,
          22
        ]
      ]
    },
    "abstract": "The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provide a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that cannot capture complex and highly-nonlinear network structures. Thus, we propose , a network fusion method based on to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method . Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/223339",
    "publisher": "bioRxiv",
    "title": "deepNF: Deep network fusion for protein function prediction",
    "URL": "https://doi.org/gcpzg7",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/223339"
  },
  {
    "type": "article-journal",
    "id": "1EdCkau6d",
    "author": [
      {
        "family": "Goldsborough",
        "given": "Peter"
      },
      {
        "family": "Pawlowski",
        "given": "Nick"
      },
      {
        "family": "Caicedo",
        "given": "Juan C"
      },
      {
        "family": "Singh",
        "given": "Shantanu"
      },
      {
        "family": "Carpenter",
        "given": "Anne E"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          12,
          2
        ]
      ]
    },
    "abstract": "We explore the application of Generative Adversarial Networks to the domain of morphological profiling of human cultured cells imaged by fluorescence microscopy. When evaluated for their ability to group cell images responding to treatment by chemicals of known classes, we find that adversarially learned representations are superior to autoencoder-based approaches. While currently inferior to classical computer vision and transfer learning, the adversarial framework enables useful visualization of the variation of cellular images due to their generative capabilities.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/227645",
    "publisher": "bioRxiv",
    "title": "CytoGAN: Generative Modeling of Cell Images",
    "URL": "https://doi.org/gcm44f",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/227645"
  },
  {
    "type": "article-journal",
    "id": "pZqk9gDB",
    "author": [
      {
        "family": "Keilwagen",
        "given": "Jens"
      },
      {
        "family": "Posch",
        "given": "Stefan"
      },
      {
        "family": "Grau",
        "given": "Jan"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          6,
          12
        ]
      ]
    },
    "container-title": "bioRxiv",
    "DOI": "10.1101/230011",
    "publisher": "bioRxiv",
    "title": "Learning from mistakes: Accurate prediction of cell type-specific transcription factor binding",
    "URL": "https://doi.org/gcsmgk",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/230011"
  },
  {
    "type": "article-journal",
    "id": "jTh3Ds6m",
    "author": [
      {
        "family": "Iglovikov",
        "given": "Vladimir"
      },
      {
        "family": "Rakhlin",
        "given": "Alexander"
      },
      {
        "family": "Kalinin",
        "given": "Alexandr A."
      },
      {
        "family": "Shvets",
        "given": "Alexey"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          6,
          20
        ]
      ]
    },
    "abstract": "Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from the 2017 Pediatric Bone Age Challenge organized by the Radiological Society of North America. The dataset for this competition consists of 12,600 radiological images. Each radiograph in this dataset is an image of a left hand labeled with bone age and sex of a patient. Our approach utilizes several deep neural network architectures trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and prediction. This approach allows us to measure the importance of specific hand bones for automated bone age analysis. We further evaluate the performance of the suggested method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/234120",
    "publisher": "bioRxiv",
    "title": "Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks",
    "URL": "https://doi.org/gc3cfb",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/234120"
  },
  {
    "type": "article-journal",
    "id": "4L1QgpXP",
    "author": [
      {
        "family": "Rakhlin",
        "given": "Alexander"
      },
      {
        "family": "Shvets",
        "given": "Alexey"
      },
      {
        "family": "Iglovikov",
        "given": "Vladimir"
      },
      {
        "family": "Kalinin",
        "given": "Alexandr A."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          4,
          2
        ]
      ]
    },
    "abstract": "Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class classification task, we report 87.2% accuracy. For 2-class classification task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image classification. The source code for our approach is made publicly available at",
    "container-title": "bioRxiv",
    "DOI": "10.1101/259911",
    "publisher": "bioRxiv",
    "title": "Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis",
    "URL": "https://doi.org/gc3cfc",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/259911"
  },
  {
    "type": "article-journal",
    "id": "16s8GKCdZ",
    "author": [
      {
        "family": "AlQuraishi",
        "given": "Mohammed"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          8,
          29
        ]
      ]
    },
    "abstract": "Predicting protein structure from sequence is a central challenge of biochemistry. Co‐evolution methods show promise, but an explicit sequence‐to‐structure map remains elusive. Advances in deep learning that replace complex, human‐designed pipelines with differentiable models optimized end‐to‐end suggest the potential benefits of similarly reformulating structure prediction. Here we report the first end‐to‐end differentiable model of protein structure. The model couples local and global protein structure via geometric units that optimize global geometry without violating local covalent chemistry. We test our model using two challenging tasks: predicting novel folds without co‐evolutionary data and predicting known folds without structural templates. In the first task the model achieves state‐of‐the‐art accuracy and in the second it comes within 1‐2Å; competing methods using co‐evolution and experimental templates have been refined over many years and it is likely that the differentiable approach has substantial room for further improvement, with applications ranging from drug discovery to protein design.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/265231",
    "publisher": "bioRxiv",
    "title": "End-to-end differentiable learning of protein structure",
    "URL": "https://doi.org/gc3gsf",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/265231"
  },
  {
    "type": "article-journal",
    "id": "5CsWRjfp",
    "author": [
      {
        "family": "Hu",
        "given": "Qiwen"
      },
      {
        "family": "Greene",
        "given": "Casey S."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          9,
          20
        ]
      ]
    },
    "abstract": "Single-cell RNA sequencing (scRNA-seq) is a powerful tool to profile the transcriptomes of a large number of individual cells at a high resolution. These data usually contain measurements of gene expression for many genes in thousands or tens of thousands of cells, though some datasets now reach the million-cell mark. Projecting high-dimensional scRNA-seq data into a low dimensional space aids downstream analysis and data visualization. Many recent preprints accomplish this using variational autoencoders (VAE), generative models that learn underlying structure of data by compress it into a constrained, low dimensional space. The low dimensional spaces generated by VAEs have revealed complex patterns and novel biological signals from large-scale gene expression data and drug response predictions. Here, we evaluate a simple VAE approach for gene expression data, Tybalt, by training and measuring its performance on sets of simulated scRNA-seq data. We find a number of counter-intuitive performance features: i.e., deeper neural networks can struggle when datasets contain more observations under some parameter configurations. We show that these methods are highly sensitive to parameter tuning: when tuned, the performance of the Tybalt model, which was not optimized for scRNA-seq data, outperforms other popular dimension reduction approaches – PCA, ZIFA, UMAP and t-SNE. On the other hand, without tuning performance can also be remarkably poor on the same data. Our results should discourage authors and reviewers from relying on self-reported performance comparisons to evaluate the relative value of contributions in this area at this time. Instead, we recommend that attempts to compare or benchmark autoencoder methods for scRNA-seq data be performed by disinterested third parties or by methods developers only on unseen benchmark data that are provided to all participants simultaneously because the potential for performance differences due to unequal parameter tuning is so high.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/385534",
    "publisher": "bioRxiv",
    "title": "Parameter tuning is a key part of dimensionality reduction via deep variational autoencoders for single cell RNA transcriptomics",
    "URL": "https://doi.org/gdxxjf",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/385534"
  },
  {
    "type": "article-journal",
    "id": "17sl0Ti0w",
    "author": [
      {
        "family": "Ni",
        "given": "Peng"
      },
      {
        "family": "Huang",
        "given": "Neng"
      },
      {
        "family": "Luo",
        "given": "Feng"
      },
      {
        "family": "Wang",
        "given": "Jianxin"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          8,
          6
        ]
      ]
    },
    "abstract": "The Oxford Nanopore sequencing enables to directly detect methylation sites in DNA from reads without extra laboratory techniques. In this study, we develop DeepSignal, a deep learning method to detect DNA methylated sites from Nanopore sequencing reads. DeepSignal construct features from both raw electrical signals and signal sequences in Nanopore reads. Testing on Nanopore reads of pUC19, E. coli and human, we show that DeepSignal can achieve both higher read level and genome level accuracy on detecting 6mA and 5mC methylation comparing to previous HMM based methods. Moreover, DeepSignal achieves similar performance cross different methylation bases and different methylation motifs. Furthermore, DeepSignal can detect 5mC and 6mA methylation states of genome sites with above 90% genome level accuracy under just 5X coverage using controlled methylation data.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/385849",
    "publisher": "bioRxiv",
    "title": "DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning",
    "URL": "https://doi.org/gf2f8t",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/385849"
  },
  {
    "type": "article-journal",
    "id": "WWSBQM3",
    "author": [
      {
        "family": "Qiu",
        "given": "Yeping Lina"
      },
      {
        "family": "Zheng",
        "given": "Hong"
      },
      {
        "family": "Gevaert",
        "given": "Olivier"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          9,
          3
        ]
      ]
    },
    "container-title": "bioRxiv",
    "DOI": "10.1101/406066",
    "publisher": "bioRxiv",
    "title": "A deep learning framework for imputing missing values in genomic data",
    "URL": "https://doi.org/gf2f8v",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/406066"
  },
  {
    "type": "article-journal",
    "id": "7pBVQEZ4",
    "author": [
      {
        "family": "Titus",
        "given": "Alexander J."
      },
      {
        "family": "Wilkins",
        "given": "Owen M."
      },
      {
        "family": "Bobak",
        "given": "Carly A."
      },
      {
        "family": "Christensen",
        "given": "Brock C."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          11,
          7
        ]
      ]
    },
    "abstract": "Recent advances in deep learning, particularly unsupervised approaches, have shown promise for furthering our biological knowledge through their application to gene expression datasets, though applications to epigenomic data are lacking. Here, we employ an unsupervised deep learning framework with variational autoencoders (VAEs) to learn latent representations of the DNA methylation landscape from three independent breast tumor datasets. Through interrogation of methylation-based learned latent dimension activation values, we demonstrate the feasibility of VAEs to track representative differential methylation patterns among clinical subtypes of tumors. CpGs whose methylation was most correlated VAE latent dimension activation values were significantly enriched for CpG sparse regulatory regions of the genome including enhancer regions. In addition, through comparison with LASSO, we show the utility of the VAE approach for revealing novel information about CpG DNA methylation patterns in breast cancer.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/433763",
    "publisher": "bioRxiv",
    "title": "Unsupervised deep learning with variational autoencoders applied to breast tumor genome-wide DNA methylation data with biologic feature extraction",
    "URL": "https://doi.org/gf2hfb",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/433763"
  },
  {
    "type": "article-journal",
    "id": "GbgdMBjb",
    "author": [
      {
        "family": "Pan",
        "given": "Xingxin"
      },
      {
        "family": "Liu",
        "given": "Biao"
      },
      {
        "family": "Wen",
        "given": "Xingzhao"
      },
      {
        "family": "Liu",
        "given": "Yulu"
      },
      {
        "family": "Zhang",
        "given": "Xiuqing"
      },
      {
        "family": "Li",
        "given": "Shengbin"
      },
      {
        "family": "Li",
        "given": "Shuaicheng"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          11,
          24
        ]
      ]
    },
    "container-title": "bioRxiv",
    "DOI": "10.1101/438218",
    "publisher": "bioRxiv",
    "title": "D-GPM: a deep learning method for gene promoter methylation inference",
    "URL": "https://doi.org/gfh798",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/438218"
  },
  {
    "type": "article-journal",
    "id": "DSTA8ohX",
    "author": [
      {
        "family": "Momeni",
        "given": "Alexandre"
      },
      {
        "family": "Thibault",
        "given": "Marc"
      },
      {
        "family": "Gevaert",
        "given": "Olivier"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          10,
          14
        ]
      ]
    },
    "abstract": "Histopathology defines the gold standard in oncology. Automatic analysis of pathology images could thus have a significant impact on diagnoses, prognoses and treatment decisions for cancer patients. Recently, convolutional neural networks (CNNs) have shown strong performance in computational histopathology tasks. However, given it remains intractable to process pathology slides in their entirety, CNNs have traditionally performed inference on small individual patches extracted from the image. This often requires a significant amount of computation and can result in ignoring potentially relevant spatial and contextual information. Being able to process larger input patches and locating discriminatory regions more efficiently could help improve both computational and task specific performance. Inspired by the recent success of Deep Recurrent Attention Models (DRAMs) in image recognition tasks, we propose a novel attention-based architecture for classification in histopathology. Similar to CNNs, DRAMs have a degree of translation invariance built-in, but the amount of computation performed can be controlled independently from the input image size. The model is a deep recurrent neural network trained with reinforcement learning to attend to the most relevant areas of large input patches. We evaluate our model on histological and molecular subtype classification tasks for the glioma cohorts of The Cancer Genome Atlas (TCGA). Our results suggest that the DRAM has comparable performance to state-of-the-art CNNs despite only processing a select number of patches.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/438341",
    "publisher": "bioRxiv",
    "title": "Deep Recurrent Attention Models for Histopathological Image Analysis",
    "URL": "https://doi.org/gf2f8w",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/438341"
  },
  {
    "type": "article-journal",
    "id": "1szm3mxk",
    "author": [
      {
        "family": "Levy-Jurgenson",
        "given": "Alona"
      },
      {
        "family": "Tekpli",
        "given": "Xavier"
      },
      {
        "family": "Kristensen",
        "given": "Vessela N."
      },
      {
        "family": "Yakhini",
        "given": "Zohar"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          12,
          9
        ]
      ]
    },
    "container-title": "bioRxiv",
    "DOI": "10.1101/491357",
    "publisher": "bioRxiv",
    "title": "Predicting Methylation from Sequence and Gene Expression Using Deep Learning with Attention",
    "URL": "https://doi.org/gf2f8x",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/491357"
  },
  {
    "type": "article-journal",
    "id": "L0qjpS0U",
    "author": [
      {
        "family": "Peng",
        "given": "Jianhao"
      },
      {
        "family": "Ochoa",
        "given": "Idoia"
      },
      {
        "family": "Milenkovic",
        "given": "Olgica"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2019,
          1,
          22
        ]
      ]
    },
    "abstract": "We focus on the new problem of determining which methylation patterns in gene promoters strongly associate with gene expression in cancer cells of different types. Although a number of results regarding the influence of methylation on expression data have been reported in the literature, our approach is unique in so far that it retrospectively predicts the combinations of methylated sites in promoter regions of genes that are reflected in the expression data. Reversing the traditional prediction order in many cases makes estimation of the model parameters easier, as real-valued data are used to predict categorical data, rather than vice-versa; in addition, our approach allows one to better assess the overall influence of methylation in modulating expression via state-of-the-art learning methods. For this purpose, we developed a novel neural network learning framework termed E M (Expression-to-Methylation) to predict the status of different methylation sites in promoter regions of several bio-marker genes based on a sufficient statistics of the whole gene expression captured through Landmark genes. We ran our experiments on unquantized and quantized expression sets and neural network weights to illustrate the robustness of the method and reduce the storage footprint of the processing pipeline.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/527044",
    "publisher": "bioRxiv",
    "title": "E <sup>2</sup> M: A Deep Learning Framework for Associating Combinatorial Methylation Patterns with Gene Expression: Supplementary",
    "URL": "https://doi.org/gf2hjm",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/527044"
  },
  {
    "type": "article-journal",
    "id": "epufolpH",
    "author": [
      {
        "family": "Levy",
        "given": "Joshua J"
      },
      {
        "family": "Titus",
        "given": "Alexander J"
      },
      {
        "family": "Petersen",
        "given": "Curtis L"
      },
      {
        "family": "Chen",
        "given": "David"
      },
      {
        "family": "Salas",
        "given": "Lucas A"
      },
      {
        "family": "Christensen",
        "given": "Brock C"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2019,
          12,
          17
        ]
      ]
    },
    "abstract": "Background: DNA methylation (DNAm) is an epigenetic regulator of gene expression programs that can be altered by environmental exposures, aging, and in pathogenesis. Traditional analyses that associate DNAm alterations with phenotypes suffer from multiple hypothesis testing and multi-collinearity due to the high-dimensional, continuous, interacting and non-linear nature of the data. Deep learning analyses have shown much promise to study disease heterogeneity. DNAm deep learning approaches have not yet been formalized into user-friendly frameworks for execution, training, and interpreting models. Here, we describe MethylNet, a DNAm deep learning method that can construct embeddings, make predictions, generate new data, and uncover unknown heterogeneity with minimal user supervision. Results: The results of our experiments indicate that MethylNet can study cellular differences, grasp higher order information of cancer sub-types, estimate age and capture factors associated with smoking in concordance with known differences. Conclusion: The ability of MethylNet to capture nonlinear interactions presents an opportunity for further study of unknown disease, cellular heterogeneity and aging processes.",
    "container-title": "bioRxiv",
    "DOI": "10.1101/692665",
    "publisher": "bioRxiv",
    "title": "MethylNet: An Automated and Modular Deep Learning Approach for DNA Methylation Analysis",
    "URL": "https://doi.org/gf48g5",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/692665"
  },
  {
    "type": "article-journal",
    "id": "G0J9P3Ln",
    "author": [
      {
        "family": "Yamashita",
        "given": "R."
      },
      {
        "family": "Sathira",
        "given": "N. P."
      },
      {
        "family": "Kanai",
        "given": "A."
      },
      {
        "family": "Tanimoto",
        "given": "K."
      },
      {
        "family": "Arauchi",
        "given": "T."
      },
      {
        "family": "Tanaka",
        "given": "Y."
      },
      {
        "family": "Hashimoto",
        "given": "S.-i."
      },
      {
        "family": "Sugano",
        "given": "S."
      },
      {
        "family": "Nakai",
        "given": "K."
      },
      {
        "family": "Suzuki",
        "given": "Y."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2011,
          3,
          3
        ]
      ]
    },
    "container-title": "Genome Research",
    "DOI": "10.1101/gr.110254.110",
    "volume": "21",
    "issue": "5",
    "page": "775-789",
    "publisher": "Cold Spring Harbor Laboratory",
    "title": "Genome-wide characterization of transcriptional start sites in humans by integrative transcriptome analysis",
    "URL": "https://doi.org/cf7nzg",
    "PMCID": "PMC3083095",
    "PMID": "21372179",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/gr.110254.110"
  },
  {
    "type": "article-journal",
    "id": "gsSLr9vf",
    "author": [
      {
        "family": "Arvey",
        "given": "A."
      },
      {
        "family": "Agius",
        "given": "P."
      },
      {
        "family": "Noble",
        "given": "W. S."
      },
      {
        "family": "Leslie",
        "given": "C."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2012,
          9,
          5
        ]
      ]
    },
    "container-title": "Genome Research",
    "DOI": "10.1101/gr.127712.111",
    "volume": "22",
    "issue": "9",
    "page": "1723-1734",
    "publisher": "Cold Spring Harbor Laboratory",
    "title": "Sequence and chromatin determinants of cell-type-specific transcription factor binding",
    "URL": "https://doi.org/f37s7h",
    "PMCID": "PMC3431489",
    "PMID": "22955984",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/gr.127712.111"
  },
  {
    "type": "article-journal",
    "id": "2CbHXoFn",
    "author": [
      {
        "family": "Kelley",
        "given": "David R."
      },
      {
        "family": "Snoek",
        "given": "Jasper"
      },
      {
        "family": "Rinn",
        "given": "John L."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          7
        ]
      ]
    },
    "container-title": "Genome Research",
    "DOI": "10.1101/gr.200535.115",
    "volume": "26",
    "issue": "7",
    "page": "990-999",
    "publisher": "Cold Spring Harbor Laboratory",
    "title": "Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks",
    "URL": "https://doi.org/f8sw35",
    "PMCID": "PMC4937568",
    "PMID": "27197224",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/gr.200535.115"
  },
  {
    "type": "article-journal",
    "id": "1Ckbonoy0",
    "author": [
      {
        "family": "Salas",
        "given": "Lucas A."
      },
      {
        "family": "Wiencke",
        "given": "John K."
      },
      {
        "family": "Koestler",
        "given": "Devin C."
      },
      {
        "family": "Zhang",
        "given": "Ze"
      },
      {
        "family": "Christensen",
        "given": "Brock C."
      },
      {
        "family": "Kelsey",
        "given": "Karl T."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018,
          9
        ]
      ]
    },
    "container-title": "Genome Research",
    "DOI": "10.1101/gr.233213.117",
    "volume": "28",
    "issue": "9",
    "page": "1285-1295",
    "publisher": "Cold Spring Harbor Laboratory",
    "title": "Tracing human stem cell lineage during development using DNA methylation",
    "URL": "https://doi.org/gd27pd",
    "PMCID": "PMC6120629",
    "PMID": "30072366",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/gr.233213.117"
  },
  {
    "type": "article-journal",
    "id": "1HhqhBwrM",
    "author": [
      {
        "family": "Abe",
        "given": "T."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2003,
          4,
          1
        ]
      ]
    },
    "container-title": "Genome Research",
    "DOI": "10.1101/gr.634603",
    "volume": "13",
    "issue": "4",
    "page": "693-702",
    "publisher": "Cold Spring Harbor Laboratory",
    "title": "Informatics for Unveiling Hidden Genome Signatures",
    "URL": "https://doi.org/c475s4",
    "PMCID": "PMC430167",
    "PMID": "12671005",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1101/gr.634603"
  },
  {
    "type": "article-journal",
    "id": "1G5eCiq4d",
    "author": [
      {
        "family": "Block",
        "given": "H. D."
      },
      {
        "family": "Knight",
        "given": "B. W."
      },
      {
        "family": "Rosenblatt",
        "given": "F."
      }
    ],
    "issued": {
      "date-parts": [
        [
          1962,
          1,
          1
        ]
      ]
    },
    "container-title": "Reviews of Modern Physics",
    "DOI": "10.1103/revmodphys.34.135",
    "volume": "34",
    "issue": "1",
    "page": "135-142",
    "publisher": "(:unav)",
    "title": "Analysis of a Four-Layer Series-Coupled Perceptron. II",
    "URL": "https://doi.org/fhx4tr",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1103/revmodphys.34.135"
  },
  {
    "type": "article-journal",
    "id": "9SnNyc8Y",
    "author": [
      {
        "family": "Chryssolouris",
        "given": "G."
      },
      {
        "family": "Lee",
        "given": "M."
      },
      {
        "family": "Ramsey",
        "given": "A."
      }
    ],
    "container-title": "IEEE Transactions on Neural Networks",
    "DOI": "10.1109/72.478409",
    "volume": "7",
    "issue": "1",
    "page": "229-232",
    "publisher": "Institute of Electrical and Electronics Engineers (IEEE)",
    "title": "Confidence interval prediction for neural network models",
    "URL": "https://doi.org/cfc8m8",
    "PMID": "18255575",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1109/72.478409"
  },
  {
    "type": "article-journal",
    "id": "fOaBA9Vc",
    "author": [
      {
        "family": "Huddar",
        "given": "Vijay"
      },
      {
        "family": "Desiraju",
        "given": "Bapu Koundinya"
      },
      {
        "family": "Rajan",
        "given": "Vaibhav"
      },
      {
        "family": "Bhattacharya",
        "given": "Sakyajit"
      },
      {
        "family": "Roy",
        "given": "Shourya"
      },
      {
        "family": "Reddy",
        "given": "Chandan K."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016
        ]
      ]
    },
    "container-title": "IEEE Access",
    "DOI": "10.1109/access.2016.2618775",
    "volume": "4",
    "page": "7988-8001",
    "publisher": "Institute of Electrical and Electronics Engineers (IEEE)",
    "title": "Predicting Complications in Critical Care Using Heterogeneous Clinical Data",
    "URL": "https://doi.org/gcgk94",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1109/access.2016.2618775"
  },
  {
    "type": "article-journal",
    "id": "TeIxEjqm",
    "author": [
      {
        "family": "Wang",
        "given": "Caihua"
      },
      {
        "family": "Liu",
        "given": "Juan"
      },
      {
        "family": "Luo",
        "given": "Fei"
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    "DOI": "10.1136/amiajnl-2011-000681",
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  {
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    "author": [
      {
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        "given": "Chaitanya"
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      {
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    "issue": "2",
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    "publisher": "Oxford University Press (OUP)",
    "title": "A review of approaches to identifying patient phenotype cohorts using electronic health records",
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        "given": "DAVID C."
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      {
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        "given": "MELINDA C."
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      {
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        "given": "JOSHUA C."
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    ],
    "issued": {
      "date-parts": [
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          1
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    "container-title": "World Scientific Pub Co Pte Lt",
    "DOI": "10.1142/9789813207813_0050",
    "publisher": "World Scientific Pub Co Pte Lt",
    "title": "STRATEGIES FOR EQUITABLE PHARMACOGENOMIC-GUIDED WARFARIN DOSING AMONG EUROPEAN AND AFRICAN AMERICAN INDIVIDUALS IN A CLINICAL POPULATION",
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    "container-title": "World Scientific Pub Co Pte Lt",
    "DOI": "10.1142/9789813235533_0008",
    "publisher": "World Scientific Pub Co Pte Lt",
    "title": "Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders",
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          11
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    "container-title": "World Scientific Pub Co Pte Lt",
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    "publisher": "World Scientific Pub Co Pte Lt",
    "title": "UNSUPERVISED FEATURE CONSTRUCTION AND KNOWLEDGE EXTRACTION FROM GENOME-WIDE ASSAYS OF BREAST CANCER WITH DENOISING AUTOENCODERS",
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    ],
    "issued": {
      "date-parts": [
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          8
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      ]
    },
    "abstract": "Next-generation sequencing technologies have allowed researchers to determine the collective genomes of microbial communities co-existing within diverse ecological environments. Varying species abundance, length and complexities within different communities, coupled with discovery of new species makes the problem of taxonomic assignment to short DNA sequence reads extremely challenging. We have developed a new sequence composition-based taxonomic classifier using extreme learning machines referred to as TAC-ELM for metagenomic analysis. TAC-ELM uses the framework of extreme learning machines to quickly and accurately learn the weights for a neural network model. The input features consist of GC content and oligonucleotides. TAC-ELM is evaluated on two metagenomic benchmarks with sequence read lengths reflecting the traditional and current sequencing technologies. Our empirical results indicate the strength of the developed approach, which outperforms state-of-the-art taxonomic classifiers in terms of accuracy and implementation complexity. We also perform experiments that evaluate the pervasive case within metagenome analysis, where a species may not have been previously sequenced or discovered and will not exist in the reference genome databases. TAC-ELM was also combined with BLAST to show improved classification results. Code and Supplementary Results: http://www.cs.gmu.edu/~mlbio/TAC-ELM (BSD License).",
    "container-title": "Journal of Bioinformatics and Computational Biology",
    "DOI": "10.1142/s0219720012500151",
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    "issue": "05",
    "page": "1250015",
    "publisher": "World Scientific Pub Co Pte Lt",
    "title": "METAGENOMIC TAXONOMIC CLASSIFICATION USING EXTREME LEARNING MACHINES",
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        "family": "Nie",
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        "given": "Wenge"
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        "family": "Zhang",
        "given": "Yiyuan"
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      {
        "family": "Ouyang",
        "given": "Yuanxin"
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        "family": "Xiong",
        "given": "Zhang"
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      "date-parts": [
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          2015,
          5,
          15
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    "abstract": "Molecular events normally have significant meanings since they describe important biological interactions or alternations such as binding of a protein. As a crucial step of biological event extraction, event trigger identification has attracted much attention and many methods have been proposed. Traditionally those methods can be categorised into rule-based approach and machine learning approach and machine learning-based approaches have demonstrated its potential and outperformed rule-based approaches in many situations. However, machine learning-based approaches still face several challenges among which a notable one is how to model semantic and syntactic information of different words and incorporate it into the prediction model. There exist many ways to model semantic and syntactic information, among which word embedding is an effective one. Therefore, in order to address this challenge, in this study, a word embedding assisted neural network prediction model is proposed to conduct event trigger identification. The experimental study on commonly used dataset has shown its potential. It is believed that this study could offer researchers insights into semantic-aware solutions for event trigger identification.",
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    "volume": "13",
    "issue": "03",
    "page": "1541001",
    "publisher": "World Scientific Pub Co Pte Lt",
    "title": "Embedding assisted prediction architecture for event trigger identification",
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        "family": "Lakhani",
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          8
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    "container-title": "Radiology",
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    "page": "574-582",
    "publisher": "Radiological Society of North America (RSNA)",
    "title": "Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks",
    "URL": "https://doi.org/gbp274",
    "PMID": "28436741",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1148/radiol.2017162326"
  },
  {
    "type": "article-journal",
    "id": "3JyJ3DTh",
    "author": [
      {
        "family": "Belle",
        "given": "Ashwin"
      },
      {
        "family": "Kon",
        "given": "Mark A."
      },
      {
        "family": "Najarian",
        "given": "Kayvan"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2013
        ]
      ]
    },
    "abstract": "The volumes of current patient data as well as their complexity make clinical decision making more challenging than ever for physicians and other care givers. This situation calls for the use of biomedical informatics methods to process data and form recommendations and/or predictions to assist such decision makers. The design, implementation, and use of biomedical informatics systems in the form of computer-aided decision support have become essential and widely used over the last two decades. This paper provides a brief review of such systems, their application protocols and methodologies, and the future challenges and directions they suggest.",
    "container-title": "The Scientific World Journal",
    "DOI": "10.1155/2013/769639",
    "volume": "2013",
    "page": "1-8",
    "publisher": "Hindawi Limited",
    "title": "Biomedical Informatics for Computer-Aided Decision Support Systems: A Survey",
    "URL": "https://doi.org/gb7x73",
    "PMCID": "PMC3575619",
    "PMID": "23431259",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1155/2013/769639"
  },
  {
    "type": "article-journal",
    "id": "14uLNRP38",
    "author": [
      {
        "family": "Tang",
        "given": "Buzhou"
      },
      {
        "family": "Cao",
        "given": "Hongxin"
      },
      {
        "family": "Wang",
        "given": "Xiaolong"
      },
      {
        "family": "Chen",
        "given": "Qingcai"
      },
      {
        "family": "Xu",
        "given": "Hua"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2014
        ]
      ]
    },
    "abstract": "Biomedical Named Entity Recognition (BNER), which extracts important entities such as genes and proteins, is a crucial step of natural language processing in the biomedical domain. Various machine learning-based approaches have been applied to BNER tasks and showed good performance. In this paper, we systematically investigated three different types of word representation (WR) features for BNER, including clustering-based representation, distributional representation, and word embeddings. We selected one algorithm from each of the three types of WR features and applied them to the JNLPBA and BioCreAtIvE II BNER tasks. Our results showed that all the three WR algorithms were beneficial to machine learning-based BNER systems. Moreover, combining these different types of WR features further improved BNER performance, indicating that they are complementary to each other. By combining all the three types of WR features, the improvements in -measure on the BioCreAtIvE II GM and JNLPBA corpora were 3.75% and 1.39%, respectively, when compared with the systems using baseline features. To the best of our knowledge, this is the first study to systematically evaluate the effect of three different types of WR features for BNER tasks.",
    "container-title": "BioMed Research International",
    "DOI": "10.1155/2014/240403",
    "volume": "2014",
    "page": "1-6",
    "publisher": "Hindawi Limited",
    "title": "Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks",
    "URL": "https://doi.org/gb76d4",
    "PMCID": "PMC3963372",
    "PMID": "24729964",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1155/2014/240403"
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  {
    "type": "article-journal",
    "id": "ULZPgbOq",
    "author": [
      {
        "family": "Quan",
        "given": "Chanqin"
      },
      {
        "family": "Hua",
        "given": "Lei"
      },
      {
        "family": "Sun",
        "given": "Xiao"
      },
      {
        "family": "Bai",
        "given": "Wenjun"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016
        ]
      ]
    },
    "abstract": "The plethora of biomedical relations which are embedded in medical logs (records) demands researchers’ attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of and data sparseness and the unattainable automation process in feature extraction. To address aforementioned issues, in this work, we propose a multichannel convolutional neural network (MCCNN) for automated biomedical relation extraction. The proposed model has the following two contributions: it enables the fusion of multiple (e.g., five) versions in word embeddings; the need for manual feature engineering can be obviated by automated feature learning with convolutional neural network (CNN). We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall -score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on -scores.",
    "container-title": "BioMed Research International",
    "DOI": "10.1155/2016/1850404",
    "volume": "2016",
    "page": "1-10",
    "publisher": "Hindawi Limited",
    "title": "Multichannel Convolutional Neural Network for Biological Relation Extraction",
    "URL": "https://doi.org/f9kqr3",
    "PMCID": "PMC5174749",
    "PMID": "28053977",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1155/2016/1850404"
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  {
    "type": "article-journal",
    "id": "19r6xFsZQ",
    "author": [
      {
        "family": "Hua",
        "given": "Lei"
      },
      {
        "family": "Quan",
        "given": "Chanqin"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016
        ]
      ]
    },
    "abstract": "The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method only takes the sdp and word embedding as input and could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task.",
    "container-title": "BioMed Research International",
    "DOI": "10.1155/2016/8479587",
    "volume": "2016",
    "page": "1-9",
    "publisher": "Hindawi Limited",
    "title": "A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction",
    "URL": "https://doi.org/f9jkh9",
    "PMCID": "PMC4963603",
    "PMID": "27493967",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1155/2016/8479587"
  },
  {
    "type": "article-journal",
    "id": "pMCJmea0",
    "author": [
      {
        "family": "Kwabi-Addo",
        "given": "B."
      },
      {
        "family": "Chung",
        "given": "W."
      },
      {
        "family": "Shen",
        "given": "L."
      },
      {
        "family": "Ittmann",
        "given": "M."
      },
      {
        "family": "Wheeler",
        "given": "T."
      },
      {
        "family": "Jelinek",
        "given": "J."
      },
      {
        "family": "Issa",
        "given": "J.-P. J."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2007,
          7,
          1
        ]
      ]
    },
    "container-title": "Clinical Cancer Research",
    "DOI": "10.1158/1078-0432.ccr-07-0085",
    "volume": "13",
    "issue": "13",
    "page": "3796-3802",
    "publisher": "American Association for Cancer Research (AACR)",
    "title": "Age-Related DNA Methylation Changes in Normal Human Prostate Tissues",
    "URL": "https://doi.org/cnhhjw",
    "PMID": "17606710",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1158/1078-0432.ccr-07-0085"
  },
  {
    "type": "article-journal",
    "id": "pEIw87Mp",
    "author": [
      {
        "family": "Mayer",
        "given": "I. A."
      },
      {
        "family": "Abramson",
        "given": "V. G."
      },
      {
        "family": "Lehmann",
        "given": "B. D."
      },
      {
        "family": "Pietenpol",
        "given": "J. A."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2014,
          2,
          16
        ]
      ]
    },
    "container-title": "Clinical Cancer Research",
    "DOI": "10.1158/1078-0432.ccr-13-0583",
    "volume": "20",
    "issue": "4",
    "page": "782-790",
    "publisher": "American Association for Cancer Research (AACR)",
    "title": "New Strategies for Triple-Negative Breast Cancer--Deciphering the Heterogeneity",
    "URL": "https://doi.org/f5tgp5",
    "PMCID": "PMC3962777",
    "PMID": "24536073",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1158/1078-0432.ccr-13-0583"
  },
  {
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    "id": "WMwUw1o4",
    "author": [
      {
        "family": "Navlakha",
        "given": "Saket"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          2
        ]
      ]
    },
    "container-title": "Neural Computation",
    "DOI": "10.1162/neco_a_00924",
    "volume": "29",
    "issue": "2",
    "page": "287-312",
    "publisher": "MIT Press - Journals",
    "title": "Learning the Structural Vocabulary of a Network",
    "URL": "https://doi.org/f9vx8v",
    "PMID": "28030777",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1162/neco_a_00924"
  },
  {
    "type": "article-journal",
    "id": "e3vyHBV2",
    "author": [
      {
        "family": "Abràmoff",
        "given": "Michael David"
      },
      {
        "family": "Lou",
        "given": "Yiyue"
      },
      {
        "family": "Erginay",
        "given": "Ali"
      },
      {
        "family": "Clarida",
        "given": "Warren"
      },
      {
        "family": "Amelon",
        "given": "Ryan"
      },
      {
        "family": "Folk",
        "given": "James C."
      },
      {
        "family": "Niemeijer",
        "given": "Meindert"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          10,
          4
        ]
      ]
    },
    "container-title": "Investigative Opthalmology & Visual Science",
    "DOI": "10.1167/iovs.16-19964",
    "volume": "57",
    "issue": "13",
    "page": "5200",
    "publisher": "Association for Research in Vision and Ophthalmology (ARVO)",
    "title": "Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning",
    "URL": "https://doi.org/f9mr6r",
    "PMID": "27701631",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1167/iovs.16-19964"
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  {
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    "author": [
      {
        "family": "Kelley",
        "given": "David R"
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      {
        "family": "Salzberg",
        "given": "Steven L"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2010,
          11,
          2
        ]
      ]
    },
    "container-title": "BMC Bioinformatics",
    "DOI": "10.1186/1471-2105-11-544",
    "volume": "11",
    "issue": "1",
    "publisher": "Springer Science and Business Media LLC",
    "title": "Clustering metagenomic sequences with interpolated Markov models",
    "URL": "https://doi.org/fgjq98",
    "PMCID": "PMC3098094",
    "PMID": "21044341",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1186/1471-2105-11-544"
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  {
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    "author": [
      {
        "family": "Yok",
        "given": "Non G"
      },
      {
        "family": "Rosen",
        "given": "Gail L"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2011,
          1,
          13
        ]
      ]
    },
    "container-title": "BMC Bioinformatics",
    "DOI": "10.1186/1471-2105-12-20",
    "volume": "12",
    "issue": "1",
    "publisher": "Springer Science and Business Media LLC",
    "title": "Combining gene prediction methods to improve metagenomic gene annotation",
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    "author": [
      {
        "family": "Houseman",
        "given": "Eugene Andres"
      },
      {
        "family": "Accomando",
        "given": "William P"
      },
      {
        "family": "Koestler",
        "given": "Devin C"
      },
      {
        "family": "Christensen",
        "given": "Brock C"
      },
      {
        "family": "Marsit",
        "given": "Carmen J"
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      {
        "family": "Nelson",
        "given": "Heather H"
      },
      {
        "family": "Wiencke",
        "given": "John K"
      },
      {
        "family": "Kelsey",
        "given": "Karl T"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2012,
          5,
          8
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    "container-title": "BMC Bioinformatics",
    "DOI": "10.1186/1471-2105-13-86",
    "volume": "13",
    "issue": "1",
    "publisher": "Springer Science and Business Media LLC",
    "title": "DNA methylation arrays as surrogate measures of cell mixture distribution",
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    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1186/1471-2105-13-86"
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      {
        "family": "Napolitano",
        "given": "Francesco"
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      {
        "family": "Zhao",
        "given": "Yan"
      },
      {
        "family": "Moreira",
        "given": "Vânia M"
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      {
        "family": "Tagliaferri",
        "given": "Roberto"
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        "given": "Dario"
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    ],
    "issued": {
      "date-parts": [
        [
          2013,
          6,
          22
        ]
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    "container-title": "Journal of Cheminformatics",
    "DOI": "10.1186/1758-2946-5-30",
    "volume": "5",
    "issue": "1",
    "publisher": "Springer Science and Business Media LLC",
    "title": "Drug repositioning: a machine-learning approach through data integration",
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      {
        "family": "Statnikov",
        "given": "Alexander"
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      {
        "family": "Henaff",
        "given": "Mikael"
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      {
        "family": "Narendra",
        "given": "Varun"
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      {
        "family": "Konganti",
        "given": "Kranti"
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      {
        "family": "Li",
        "given": "Zhiguo"
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      {
        "family": "Yang",
        "given": "Liying"
      },
      {
        "family": "Pei",
        "given": "Zhiheng"
      },
      {
        "family": "Blaser",
        "given": "Martin J"
      },
      {
        "family": "Aliferis",
        "given": "Constantin F"
      },
      {
        "family": "Alekseyenko",
        "given": "Alexander V"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2013,
          4,
          5
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    },
    "container-title": "Microbiome",
    "DOI": "10.1186/2049-2618-1-11",
    "volume": "1",
    "issue": "1",
    "publisher": "Springer Science and Business Media LLC",
    "title": "A comprehensive evaluation of multicategory classification methods for microbiomic data",
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    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1186/2049-2618-1-11"
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  {
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    "author": [
      {
        "family": "Stein",
        "given": "Lincoln D"
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      "date-parts": [
        [
          2010
        ]
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    "container-title": "Genome Biology",
    "DOI": "10.1186/gb-2010-11-5-207",
    "volume": "11",
    "issue": "5",
    "page": "207",
    "publisher": "Springer Science and Business Media LLC",
    "title": "The case for cloud computing in genome informatics",
    "URL": "https://doi.org/ach",
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    "PMID": "20441614",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1186/gb-2010-11-5-207"
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  {
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    "author": [
      {
        "family": "Horvath",
        "given": "Steve"
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    "issued": {
      "date-parts": [
        [
          2013
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    "container-title": "Genome Biology",
    "DOI": "10.1186/gb-2013-14-10-r115",
    "volume": "14",
    "issue": "10",
    "page": "R115",
    "publisher": "Springer Science and Business Media LLC",
    "title": "DNA methylation age of human tissues and cell types",
    "URL": "https://doi.org/pcx",
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    "PMID": "24138928",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1186/gb-2013-14-10-r115"
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  {
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    "author": [
      {
        "family": "Zeng",
        "given": "Tao"
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      {
        "family": "Li",
        "given": "Rongjian"
      },
      {
        "family": "Mukkamala",
        "given": "Ravi"
      },
      {
        "family": "Ye",
        "given": "Jieping"
      },
      {
        "family": "Ji",
        "given": "Shuiwang"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2015,
          5,
          7
        ]
      ]
    },
    "container-title": "BMC Bioinformatics",
    "DOI": "10.1186/s12859-015-0553-9",
    "volume": "16",
    "issue": "1",
    "publisher": "Springer Science and Business Media LLC",
    "title": "Deep convolutional neural networks for annotating gene expression patterns in the mouse brain",
    "URL": "https://doi.org/gb8w84",
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    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1186/s12859-015-0553-9"
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  {
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    "author": [
      {
        "family": "Ding",
        "given": "Xiao"
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      {
        "family": "Cheng",
        "given": "Fudong"
      },
      {
        "family": "Cao",
        "given": "Changchang"
      },
      {
        "family": "Sun",
        "given": "Xiao"
      }
    ],
    "issued": {
      "date-parts": [
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          2015,
          10,
          7
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    "container-title": "BMC Bioinformatics",
    "DOI": "10.1186/s12859-015-0753-3",
    "volume": "16",
    "issue": "1",
    "publisher": "Springer Science and Business Media LLC",
    "title": "DectICO: an alignment-free supervised metagenomic classification method based on feature extraction and dynamic selection",
    "URL": "https://doi.org/f743hh",
    "PMCID": "PMC4596415",
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    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1186/s12859-015-0753-3"
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  {
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      {
        "family": "Ditzler",
        "given": "Gregory"
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      {
        "family": "Morrison",
        "given": "J. Calvin"
      },
      {
        "family": "Lan",
        "given": "Yemin"
      },
      {
        "family": "Rosen",
        "given": "Gail L."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2015,
          11,
          4
        ]
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    },
    "container-title": "BMC Bioinformatics",
    "DOI": "10.1186/s12859-015-0793-8",
    "volume": "16",
    "issue": "1",
    "publisher": "Springer Science and Business Media LLC",
    "title": "Fizzy: feature subset selection for metagenomics",
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    "PMID": "26538306",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1186/s12859-015-0793-8"
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    "title": "Open collaborative writing with Manubot",
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          30
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    "title": "Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties",
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    "publisher": "Public Library of Science (PLoS)",
    "title": "Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data",
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      {
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          13
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    "volume": "9",
    "issue": "3",
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    "title": "WGSQuikr: Fast Whole-Genome Shotgun Metagenomic Classification",
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          10
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    "publisher": "Public Library of Science (PLoS)",
    "title": "On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation",
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          2017,
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    "title": "DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads",
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          8,
          7
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    "volume": "12",
    "issue": "8",
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    "title": "Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning",
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      {
        "family": "Bickson",
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      {
        "family": "Gonzalez",
        "given": "Joseph"
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      {
        "family": "Guestrin",
        "given": "Carlos"
      },
      {
        "family": "Kyrola",
        "given": "Aapo"
      },
      {
        "family": "Hellerstein",
        "given": "Joseph M."
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    ],
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        [
          2012,
          4,
          1
        ]
      ]
    },
    "container-title": "Proceedings of the VLDB Endowment",
    "DOI": "10.14778/2212351.2212354",
    "volume": "5",
    "issue": "8",
    "page": "716-727",
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    "title": "Distributed GraphLab",
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      {
        "family": "Li",
        "given": "shuang"
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      {
        "family": "Huang",
        "given": "Degen"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016
        ]
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    },
    "container-title": "International Journal of Data Mining and Bioinformatics",
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    "volume": "14",
    "issue": "3",
    "page": "276",
    "publisher": "Inderscience Publishers",
    "title": "A general protein-protein interaction extraction architecture based on word representation and feature selection",
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      "date-parts": [
        [
          1953
        ]
      ]
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    "source": "Crossref",
    "title": "17. A Value for n-Person Games",
    "author": [
      {
        "given": "L. S.",
        "family": "Shapley"
      }
    ],
    "container-title": "Contributions to the Theory of Games (AM-28), Volume II",
    "URL": "https://doi.org/10.1515/9781400881970-018",
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        "given": "Macha"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          1,
          1
        ]
      ]
    },
    "abstract": "Owing to the complexity and variability of metagenomic studies, modern machine learning approaches have seen increased usage to answer a variety of question encompassing the full range of metagenomic NGS data analysis.We review here the contribution of machine learning techniques for the field of metagenomics, by presenting known successful approaches in a unified framework. This review focuses on five important metagenomic problems:OTU-clustering, binning, taxonomic proffiing and assignment, comparative metagenomics and gene prediction. For each of these problems, we identify the most prominent methods, summarize the machine learning approaches used and put them into perspective of similar methods.We conclude our review looking further ahead at the challenge posed by the analysis of interactions within microbial communities and different environments, in a field one could call “integrative metagenomics”.",
    "container-title": "Metagenomics",
    "DOI": "10.1515/metgen-2016-0001",
    "volume": "1",
    "issue": "1",
    "publisher": "Portico",
    "title": "Machine learning for metagenomics: methods and tools",
    "URL": "https://doi.org/gcgmct",
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  {
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    "author": [
      {
        "family": "Angermueller",
        "given": "Christof"
      },
      {
        "family": "Pärnamaa",
        "given": "Tanel"
      },
      {
        "family": "Parts",
        "given": "Leopold"
      },
      {
        "family": "Stegle",
        "given": "Oliver"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          7,
          29
        ]
      ]
    },
    "container-title": "Molecular Systems Biology",
    "DOI": "10.15252/msb.20156651",
    "volume": "12",
    "issue": "7",
    "page": "878",
    "publisher": "EMBO",
    "title": "Deep learning for computational biology",
    "URL": "https://doi.org/f8xtvh",
    "PMCID": "PMC4965871",
    "PMID": "27474269",
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  },
  {
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    "author": [
      {
        "family": "Kraus",
        "given": "Oren Z"
      },
      {
        "family": "Grys",
        "given": "Ben T"
      },
      {
        "family": "Ba",
        "given": "Jimmy"
      },
      {
        "family": "Chong",
        "given": "Yolanda"
      },
      {
        "family": "Frey",
        "given": "Brendan J"
      },
      {
        "family": "Boone",
        "given": "Charles"
      },
      {
        "family": "Andrews",
        "given": "Brenda J"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          4,
          18
        ]
      ]
    },
    "container-title": "Molecular Systems Biology",
    "DOI": "10.15252/msb.20177551",
    "volume": "13",
    "issue": "4",
    "page": "924",
    "publisher": "EMBO",
    "title": "Automated analysis of high‐content microscopy data with deep learning",
    "URL": "https://doi.org/f93cpr",
    "PMCID": "PMC5408780",
    "PMID": "28420678",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.15252/msb.20177551"
  },
  {
    "type": "article-journal",
    "id": "CNz9HwZ3",
    "author": [
      {
        "family": "Juan-Mateu",
        "given": "Jonàs"
      },
      {
        "family": "Villate",
        "given": "Olatz"
      },
      {
        "family": "Eizirik",
        "given": "Décio L"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          5
        ]
      ]
    },
    "abstract": "Type 1 diabetes (T1D) is a chronic autoimmune disease in which pancreatic β cells are killed by infiltrating immune cells and by cytokines released by these cells. This takes place in the context of a dysregulated dialogue between invading immune cells and target β cells, but the intracellular signals that decide β cell fate remain to be clarified. Alternative splicing (AS) is a complex post-transcriptional regulatory mechanism affecting gene expression. It regulates the inclusion/exclusion of exons into mature mRNAs, allowing individual genes to produce multiple protein isoforms that expand the proteome diversity. Functionally related transcript populations are co-ordinately spliced by master splicing factors, defining regulatory networks that allow cells to rapidly adapt their transcriptome in response to intra and extracellular cues. There is a growing interest in the role of AS in autoimmune diseases, but little is known regarding its role in T1D. In this review, we discuss recent findings suggesting that splicing events occurring in both immune and pancreatic β cells contribute to the pathogenesis of T1D. Splicing switches in T cells and in lymph node stromal cells are involved in the modulation of the immune response against β cells, while β cells exposed to pro-inflammatory cytokines activate complex splicing networks that modulate β cell viability, expression of neoantigens and susceptibility to immune-induced stress. Unveiling the role of AS in β cell functional loss and death will increase our understanding of T1D pathogenesis and may open new avenues for disease prevention and therapy.",
    "container-title": "European Journal of Endocrinology",
    "DOI": "10.1530/eje-15-0916",
    "volume": "174",
    "issue": "5",
    "page": "R225-R238",
    "publisher": "Bioscientifica",
    "title": "MECHANISMS IN ENDOCRINOLOGY: Alternative splicing: the new frontier in diabetes research",
    "URL": "https://doi.org/f8nccm",
    "PMCID": "PMC5331159",
    "PMID": "26628584",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1530/eje-15-0916"
  },
  {
    "publisher": "Genetics Society of America",
    "issue": "5",
    "DOI": "10.1534/g3.116.033654",
    "type": "article-journal",
    "page": "1385-1392",
    "source": "Crossref",
    "title": "Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning",
    "volume": "7",
    "author": [
      {
        "given": "Tanel",
        "family": "Pärnamaa"
      },
      {
        "given": "Leopold",
        "family": "Parts"
      }
    ],
    "container-title": "G3: Genes|Genomes|Genetics",
    "issued": {
      "date-parts": [
        [
          2017,
          4,
          8
        ]
      ]
    },
    "URL": "https://doi.org/10.1534/g3.116.033654",
    "container-title-short": "G3",
    "id": "2a7MHtAx",
    "note": "This CSL JSON Item was loaded by Manubot v0.3.1 from a manual reference file.\nmanual_reference_filename: manual-references.json\nstandard_id: doi:10.1534/g3.116.033654"
  },
  {
    "type": "article-journal",
    "id": "v8Lp4ibI",
    "author": [
      {
        "family": "Dwork",
        "given": "Cynthia"
      },
      {
        "family": "Roth",
        "given": "Aaron"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2013
        ]
      ]
    },
    "container-title": "Foundations and Trends® in Theoretical Computer Science",
    "DOI": "10.1561/0400000042",
    "volume": "9",
    "issue": "3-4",
    "page": "211-407",
    "publisher": "Now Publishers",
    "title": "The Algorithmic Foundations of Differential Privacy",
    "URL": "https://doi.org/gcgmcw",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1561/0400000042"
  },
  {
    "type": "article-journal",
    "id": "Fx5qVQlk",
    "author": [
      {
        "family": "Faber",
        "given": "Jorge"
      },
      {
        "family": "Fonseca",
        "given": "Lilian Martins"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2014,
          8
        ]
      ]
    },
    "abstract": "Sample size calculation is part of the early stages of conducting an epidemiological, clinical or lab study. In preparing a scientific paper, there are ethical and methodological indications for its use. Two investigations conducted with the same methodology and achieving equivalent results, but different only in terms of sample size, may point the researcher in different directions when it comes to making clinical decisions. Therefore, ideally, samples should not be small and, contrary to what one might think, should not be excessive. The aim of this paper is to discuss in clinical language the main implications of the sample size when interpreting a study.",
    "container-title": "Dental Press Journal of Orthodontics",
    "DOI": "10.1590/2176-9451.19.4.027-029.ebo",
    "volume": "19",
    "issue": "4",
    "page": "27-29",
    "publisher": "FapUNIFESP (SciELO)",
    "title": "How sample size influences research outcomes",
    "URL": "https://doi.org/gcgmcx",
    "PMCID": "PMC4296634",
    "PMID": "25279518",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.1590/2176-9451.19.4.027-029.ebo"
  },
  {
    "type": "article-journal",
    "id": "8i1LNUx2",
    "author": [
      {
        "family": "Quach",
        "given": "Austin"
      },
      {
        "family": "Levine",
        "given": "Morgan E."
      },
      {
        "family": "Tanaka",
        "given": "Toshiko"
      },
      {
        "family": "Lu",
        "given": "Ake T."
      },
      {
        "family": "Chen",
        "given": "Brian H."
      },
      {
        "family": "Ferrucci",
        "given": "Luigi"
      },
      {
        "family": "Ritz",
        "given": "Beate"
      },
      {
        "family": "Bandinelli",
        "given": "Stefania"
      },
      {
        "family": "Neuhouser",
        "given": "Marian L."
      },
      {
        "family": "Beasley",
        "given": "Jeannette M."
      },
      {
        "family": "Snetselaar",
        "given": "Linda"
      },
      {
        "family": "Wallace",
        "given": "Robert B."
      },
      {
        "family": "Tsao",
        "given": "Philip S."
      },
      {
        "family": "Absher",
        "given": "Devin"
      },
      {
        "family": "Assimes",
        "given": "Themistocles L."
      },
      {
        "family": "Stewart",
        "given": "James D."
      },
      {
        "family": "Li",
        "given": "Yun"
      },
      {
        "family": "Hou",
        "given": "Lifang"
      },
      {
        "family": "Baccarelli",
        "given": "Andrea A."
      },
      {
        "family": "Whitsel",
        "given": "Eric A."
      },
      {
        "family": "Horvath",
        "given": "Steve"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          2,
          14
        ]
      ]
    },
    "container-title": "Aging",
    "DOI": "10.18632/aging.101168",
    "volume": "9",
    "issue": "2",
    "page": "419-446",
    "publisher": "Impact Journals, LLC",
    "title": "Epigenetic clock analysis of diet, exercise, education, and lifestyle factors",
    "URL": "https://doi.org/gf282v",
    "PMCID": "PMC5361673",
    "PMID": "28198702",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.18632/aging.101168"
  },
  {
    "type": "article-journal",
    "id": "LeLKNlsR",
    "author": [
      {
        "family": "Liu",
        "given": "Yue"
      },
      {
        "family": "Ge",
        "given": "Tao"
      },
      {
        "family": "Mathews",
        "given": "Kusum"
      },
      {
        "family": "Ji",
        "given": "Heng"
      },
      {
        "family": "McGuinness",
        "given": "Deborah"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2015
        ]
      ]
    },
    "container-title": "Association for Computational Linguistics (ACL)",
    "DOI": "10.18653/v1/w15-3810",
    "publisher": "Association for Computational Linguistics (ACL)",
    "title": "Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion",
    "URL": "https://doi.org/gcgnx5",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.18653/v1/w15-3810"
  },
  {
    "type": "article-journal",
    "id": "E0XYyYBe",
    "author": [
      {
        "family": "Li",
        "given": "Chen"
      },
      {
        "family": "Song",
        "given": "Runqing"
      },
      {
        "family": "Liakata",
        "given": "Maria"
      },
      {
        "family": "Vlachos",
        "given": "Andreas"
      },
      {
        "family": "Seneff",
        "given": "Stephanie"
      },
      {
        "family": "Zhang",
        "given": "Xiangrong"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2015
        ]
      ]
    },
    "container-title": "Association for Computational Linguistics (ACL)",
    "DOI": "10.18653/v1/w15-3814",
    "publisher": "Association for Computational Linguistics (ACL)",
    "title": "Using word embedding for bio-event extraction",
    "URL": "https://doi.org/gcgnx6",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.18653/v1/w15-3814"
  },
  {
    "type": "article-journal",
    "id": "pWpK5WUq",
    "author": [
      {
        "family": "wu",
        "given": "yonghui"
      },
      {
        "family": "Xu",
        "given": "Jun"
      },
      {
        "family": "Zhang",
        "given": "Yaoyun"
      },
      {
        "family": "Xu",
        "given": "Hua"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2015
        ]
      ]
    },
    "container-title": "Association for Computational Linguistics (ACL)",
    "DOI": "10.18653/v1/w15-3822",
    "publisher": "Association for Computational Linguistics (ACL)",
    "title": "Clinical Abbreviation Disambiguation Using Neural Word Embeddings",
    "URL": "https://doi.org/gcgnx7",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.18653/v1/w15-3822"
  },
  {
    "type": "article-journal",
    "id": "1AkznVzFs",
    "author": [
      {
        "family": "Mehryary",
        "given": "Farrokh"
      },
      {
        "family": "Björne",
        "given": "Jari"
      },
      {
        "family": "Pyysalo",
        "given": "Sampo"
      },
      {
        "family": "Salakoski",
        "given": "Tapio"
      },
      {
        "family": "Ginter",
        "given": "Filip"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016
        ]
      ]
    },
    "container-title": "Association for Computational Linguistics (ACL)",
    "DOI": "10.18653/v1/w16-3009",
    "publisher": "Association for Computational Linguistics (ACL)",
    "title": "Deep Learning with Minimal Training Data: TurkuNLP Entry in the BioNLP Shared Task 2016",
    "URL": "https://doi.org/gcgnx8",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.18653/v1/w16-3009"
  },
  {
    "type": "article-journal",
    "id": "ztw1ugBP",
    "author": [
      {
        "family": "Li",
        "given": "Honglei"
      },
      {
        "family": "Zhang",
        "given": "Jianhai"
      },
      {
        "family": "Wang",
        "given": "Jian"
      },
      {
        "family": "Lin",
        "given": "Hongfei"
      },
      {
        "family": "Yang",
        "given": "Zhihao"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016
        ]
      ]
    },
    "container-title": "Association for Computational Linguistics (ACL)",
    "DOI": "10.18653/v1/w16-3012",
    "publisher": "Association for Computational Linguistics (ACL)",
    "title": "DUTIR in BioNLP-ST 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations",
    "URL": "https://doi.org/gcgnx9",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.18653/v1/w16-3012"
  },
  {
    "type": "article-journal",
    "id": "zUPTZa6w",
    "author": [
      {
        "family": "Asada",
        "given": "Masaki"
      },
      {
        "family": "Miwa",
        "given": "Makoto"
      },
      {
        "family": "Sasaki",
        "given": "Yutaka"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017
        ]
      ]
    },
    "container-title": "Association for Computational Linguistics (ACL)",
    "DOI": "10.18653/v1/w17-2302",
    "publisher": "Association for Computational Linguistics (ACL)",
    "title": "Extracting Drug-Drug Interactions with Attention CNNs",
    "URL": "https://doi.org/gcgnzb",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.18653/v1/w17-2302"
  },
  {
    "type": "article-journal",
    "id": "1H4fyFU1f",
    "author": [
      {
        "family": "Peng",
        "given": "Yifan"
      },
      {
        "family": "Lu",
        "given": "Zhiyong"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017
        ]
      ]
    },
    "container-title": "Association for Computational Linguistics (ACL)",
    "DOI": "10.18653/v1/w17-2304",
    "publisher": "Association for Computational Linguistics (ACL)",
    "title": "Deep learning for extracting protein-protein interactions from biomedical literature",
    "URL": "https://doi.org/gcgnzc",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.18653/v1/w17-2304"
  },
  {
    "type": "article-journal",
    "id": "zPnsAqyX",
    "author": [
      {
        "family": "Mohan",
        "given": "Sunil"
      },
      {
        "family": "Fiorini",
        "given": "Nicolas"
      },
      {
        "family": "Kim",
        "given": "Sun"
      },
      {
        "family": "Lu",
        "given": "Zhiyong"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017
        ]
      ]
    },
    "container-title": "Association for Computational Linguistics (ACL)",
    "DOI": "10.18653/v1/w17-2328",
    "publisher": "Association for Computational Linguistics (ACL)",
    "title": "Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs",
    "URL": "https://doi.org/gcgnzd",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.18653/v1/w17-2328"
  },
  {
    "type": "article-journal",
    "id": "8YmxYueq",
    "author": [
      {
        "family": "Lin",
        "given": "Chen"
      },
      {
        "family": "Miller",
        "given": "Timothy"
      },
      {
        "family": "Dligach",
        "given": "Dmitriy"
      },
      {
        "family": "Bethard",
        "given": "Steven"
      },
      {
        "family": "Savova",
        "given": "Guergana"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017
        ]
      ]
    },
    "container-title": "Association for Computational Linguistics (ACL)",
    "DOI": "10.18653/v1/w17-2341",
    "publisher": "Association for Computational Linguistics (ACL)",
    "title": "Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks",
    "URL": "https://doi.org/gcgnzf",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.18653/v1/w17-2341"
  },
  {
    "type": "article-journal",
    "id": "4QDXEv4C",
    "author": [
      {
        "family": "Karimi",
        "given": "Sarvnaz"
      },
      {
        "family": "Dai",
        "given": "Xiang"
      },
      {
        "family": "Hassanzadeh",
        "given": "Hamedh"
      },
      {
        "family": "Nguyen",
        "given": "Anthony"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017
        ]
      ]
    },
    "container-title": "Association for Computational Linguistics (ACL)",
    "DOI": "10.18653/v1/w17-2342",
    "publisher": "Association for Computational Linguistics (ACL)",
    "title": "Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods",
    "URL": "https://doi.org/gcgnzg",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.18653/v1/w17-2342"
  },
  {
    "type": "article-journal",
    "id": "cjMBtlVG",
    "author": [
      {
        "family": "Teschendorff",
        "given": "Andrew E"
      },
      {
        "family": "Zheng",
        "given": "Shijie C"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          5
        ]
      ]
    },
    "container-title": "Epigenomics",
    "DOI": "10.2217/epi-2016-0153",
    "volume": "9",
    "issue": "5",
    "page": "757-768",
    "publisher": "Future Medicine Ltd",
    "title": "Cell-type deconvolution in epigenome-wide association studies: a review and recommendations",
    "URL": "https://doi.org/f97bw4",
    "PMID": "28517979",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.2217/epi-2016-0153"
  },
  {
    "type": "article-journal",
    "id": "6RHepB1T",
    "author": [
      {
        "family": "Kaplan",
        "given": "E. L."
      },
      {
        "family": "Meier",
        "given": "Paul"
      }
    ],
    "issued": {
      "date-parts": [
        [
          1958,
          6
        ]
      ]
    },
    "container-title": "Journal of the American Statistical Association",
    "DOI": "10.2307/2281868",
    "volume": "53",
    "issue": "282",
    "page": "457",
    "publisher": "JSTOR",
    "title": "Nonparametric Estimation from Incomplete Observations",
    "URL": "https://doi.org/fscrh2",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.2307/2281868"
  },
  {
    "type": "article-journal",
    "id": "MmRGFVUu",
    "author": [
      {
        "family": "Kraus",
        "given": "Oren Z."
      },
      {
        "family": "Frey",
        "given": "Brendan J."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          1,
          24
        ]
      ]
    },
    "container-title": "Critical Reviews in Biochemistry and Molecular Biology",
    "DOI": "10.3109/10409238.2015.1135868",
    "volume": "51",
    "issue": "2",
    "page": "102-109",
    "publisher": "Informa UK Limited",
    "title": "Computer vision for high content screening",
    "URL": "https://doi.org/gcgmc2",
    "PMID": "26806341",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.3109/10409238.2015.1135868"
  },
  {
    "type": "article-journal",
    "id": "rw9nA3Y7",
    "author": [
      {
        "family": "Pestian",
        "given": "John P."
      },
      {
        "family": "Brew",
        "given": "Christopher"
      },
      {
        "family": "Matykiewicz",
        "given": "Paweł"
      },
      {
        "family": "Hovermale",
        "given": "D. J."
      },
      {
        "family": "Johnson",
        "given": "Neil"
      },
      {
        "family": "Cohen",
        "given": "K. Bretonnel"
      },
      {
        "family": "Duch",
        "given": "Włodzisław"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2007
        ]
      ]
    },
    "container-title": "Association for Computational Linguistics (ACL)",
    "DOI": "10.3115/1572392.1572411",
    "publisher": "Association for Computational Linguistics (ACL)",
    "title": "A shared task involving multi-label classification of clinical free text",
    "URL": "https://doi.org/b4zv3c",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.3115/1572392.1572411"
  },
  {
    "id": "sePDg4mZ",
    "type": "article-journal",
    "title": "Exploring the Application of Deep Learning Techniques on Medical Text Corpora",
    "container-title": "Studies in Health Technology and Informatics",
    "page": "584–588",
    "source": "mEDRA",
    "abstract": "With the rapidly growing amount of biomedical literature it becomes increasingly difficult to find relevant information quickly and reliably. In this study we applied the word2vec deep learning toolkit to medical corpora to test its potential for improving the accessibility of medical knowledge. We evaluated the efficiency of word2vec in identifying properties of pharmaceuticals based on mid-sized, unstructured medical text corpora without any additional background knowledge. Properties included relationships to diseases (&lsquo;may treat&rsquo;) or physiological processes (&lsquo;has physiological effect&rsquo;). We evaluated the relationships identified by word2vec through comparison with the National Drug File &ndash; Reference Terminology (NDF-RT) ontology. The results of our first evaluation were mixed, but helped us identify further avenues for employing deep learning technologies in medical information retrieval, as well as using them to complement curated knowledge captured in ontologies and taxonomies.",
    "URL": "https://doi.org/gcgnzh",
    "DOI": "10.3233/978-1-61499-432-9-584",
    "ISSN": "0926-9630",
    "author": [
      {
        "family": "Antonio",
        "given": "Minarro-Gim&eacute;nez Jos&eacute;"
      },
      {
        "family": "Oscar",
        "given": "Mar&iacute;n-Alonso"
      },
      {
        "family": "Matthias",
        "given": "Samwald"
      }
    ],
    "issued": {
      "date-parts": [
        [
          "2014"
        ]
      ]
    },
    "accessed": {
      "date-parts": [
        [
          "2020",
          2,
          29
        ]
      ]
    },
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.3233/978-1-61499-432-9-584"
  },
  {
    "type": "article-journal",
    "id": "Y1D0SZrO",
    "author": [
      {
        "family": "Mayr",
        "given": "Andreas"
      },
      {
        "family": "Klambauer",
        "given": "Günter"
      },
      {
        "family": "Unterthiner",
        "given": "Thomas"
      },
      {
        "family": "Hochreiter",
        "given": "Sepp"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          2,
          2
        ]
      ]
    },
    "container-title": "Frontiers in Environmental Science",
    "DOI": "10.3389/fenvs.2015.00080",
    "volume": "3",
    "publisher": "Frontiers Media SA",
    "title": "DeepTox: Toxicity Prediction using Deep Learning",
    "URL": "https://doi.org/gcgmc3",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.3389/fenvs.2015.00080"
  },
  {
    "id": "ppGS5h4v",
    "type": "article-journal",
    "title": "Large-scale integration of small-molecule induced genome-wide transcriptional responses, Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing systems-level drug action",
    "container-title": "Frontiers in Genetics",
    "URL": "https://doi.org/10.3389/fgene.2014.00342",
    "DOI": "10.3389/fgene.2014.00342",
    "author": [
      {
        "family": "Vidovic",
        "given": "Dušica"
      },
      {
        "family": "Koleti",
        "given": "Amar"
      },
      {
        "family": "Schürer",
        "given": "Stephan C"
      }
    ],
    "note": "This CSL JSON Item was loaded by Manubot v0.3.1 from a manual reference file.\nmanual_reference_filename: manual-references.json\nstandard_id: doi:10.3389/fgene.2014.00342"
  },
  {
    "type": "article-journal",
    "id": "WNE8N7Cp",
    "author": [
      {
        "family": "Marblestone",
        "given": "Adam H."
      },
      {
        "family": "Wayne",
        "given": "Greg"
      },
      {
        "family": "Kording",
        "given": "Konrad P."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          9,
          14
        ]
      ]
    },
    "container-title": "Frontiers in Computational Neuroscience",
    "DOI": "10.3389/fncom.2016.00094",
    "volume": "10",
    "publisher": "Frontiers Media SA",
    "title": "Toward an Integration of Deep Learning and Neuroscience",
    "URL": "https://doi.org/gcsgr2",
    "PMCID": "PMC5021692",
    "PMID": "27683554",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.3389/fncom.2016.00094"
  },
  {
    "type": "article-journal",
    "id": "OuoHShLI",
    "author": [
      {
        "family": "Liu",
        "given": "Shengyu"
      },
      {
        "family": "Tang",
        "given": "Buzhou"
      },
      {
        "family": "Chen",
        "given": "Qingcai"
      },
      {
        "family": "Wang",
        "given": "Xiaolong"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2015,
          12,
          11
        ]
      ]
    },
    "container-title": "Information",
    "DOI": "10.3390/info6040848",
    "volume": "6",
    "issue": "4",
    "page": "848-865",
    "publisher": "MDPI AG",
    "title": "Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries",
    "URL": "https://doi.org/gcgnzj",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.3390/info6040848"
  },
  {
    "type": "article-journal",
    "id": "71snAYRy",
    "author": [
      {
        "family": "Titus",
        "given": "Alexander J."
      },
      {
        "family": "Bobak",
        "given": "Carly A."
      },
      {
        "family": "Christensen",
        "given": "Brock C."
      }
    ],
    "issued": {
      "date-parts": [
        [
          2018
        ]
      ]
    },
    "container-title": "Scitepress",
    "DOI": "10.5220/0006636401400145",
    "publisher": "Scitepress",
    "title": "A New Dimension of Breast Cancer Epigenetics - Applications of Variational Autoencoders with DNA Methylation",
    "URL": "https://doi.org/gd58sg",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.5220/0006636401400145"
  },
  {
    "type": "article-journal",
    "id": "jdg2u7bX",
    "author": [
      {
        "family": "Baxt",
        "given": "William G."
      }
    ],
    "issued": {
      "date-parts": [
        [
          1991,
          12,
          1
        ]
      ]
    },
    "container-title": "Annals of Internal Medicine",
    "DOI": "10.7326/0003-4819-115-11-843",
    "volume": "115",
    "issue": "11",
    "page": "843",
    "publisher": "American College of Physicians",
    "title": "Use of an Artificial Neural Network for the Diagnosis of Myocardial Infarction",
    "URL": "https://doi.org/gcgmc5",
    "PMID": "1952470",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.7326/0003-4819-115-11-843"
  },
  {
    "type": "article-journal",
    "id": "ZxQ49E8q",
    "author": [
      {
        "family": "Errington",
        "given": "Timothy M"
      },
      {
        "family": "Iorns",
        "given": "Elizabeth"
      },
      {
        "family": "Gunn",
        "given": "William"
      },
      {
        "family": "Tan",
        "given": "Fraser Elisabeth"
      },
      {
        "family": "Lomax",
        "given": "Joelle"
      },
      {
        "family": "Nosek",
        "given": "Brian A"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2014,
          12,
          10
        ]
      ]
    },
    "abstract": "It is widely believed that research that builds upon previously published findings has reproduced the original work. However, it is rare for researchers to perform or publish direct replications of existing results. The Reproducibility Project: Cancer Biology is an open investigation of reproducibility in preclinical cancer biology research. We have identified 50 high impact cancer biology articles published in the period 2010-2012, and plan to replicate a subset of experimental results from each article. A Registered Report detailing the proposed experimental designs and protocols for each subset of experiments will be peer reviewed and published prior to data collection. The results of these experiments will then be published in a Replication Study. The resulting open methodology and dataset will provide evidence about the reproducibility of high-impact results, and an opportunity to identify predictors of reproducibility.",
    "container-title": "eLife",
    "DOI": "10.7554/elife.04333",
    "volume": "3",
    "publisher": "eLife Sciences Publications, Ltd",
    "title": "An open investigation of the reproducibility of cancer biology research",
    "URL": "https://doi.org/gcnzrq",
    "PMCID": "PMC4270077",
    "PMID": "25490932",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.7554/elife.04333"
  },
  {
    "type": "article-journal",
    "id": "12vPQi3gp",
    "author": [
      {
        "family": "Agarwal",
        "given": "Vikram"
      },
      {
        "family": "Bell",
        "given": "George W"
      },
      {
        "family": "Nam",
        "given": "Jin-Wu"
      },
      {
        "family": "Bartel",
        "given": "David P"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2015,
          8,
          12
        ]
      ]
    },
    "abstract": "MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks.",
    "container-title": "eLife",
    "DOI": "10.7554/elife.05005",
    "volume": "4",
    "publisher": "eLife Sciences Publications, Ltd",
    "title": "Predicting effective microRNA target sites in mammalian mRNAs",
    "URL": "https://doi.org/gcgmc6",
    "PMCID": "PMC4532895",
    "PMID": "26267216",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.7554/elife.05005"
  },
  {
    "type": "article-journal",
    "id": "vk9ZInF3",
    "author": [
      {
        "family": "Regev",
        "given": "Aviv"
      },
      {
        "family": "Teichmann",
        "given": "Sarah A"
      },
      {
        "family": "Lander",
        "given": "Eric S"
      },
      {
        "family": "Amit",
        "given": "Ido"
      },
      {
        "family": "Benoist",
        "given": "Christophe"
      },
      {
        "family": "Birney",
        "given": "Ewan"
      },
      {
        "family": "Bodenmiller",
        "given": "Bernd"
      },
      {
        "family": "Campbell",
        "given": "Peter"
      },
      {
        "family": "Carninci",
        "given": "Piero"
      },
      {
        "family": "Clatworthy",
        "given": "Menna"
      },
      {
        "family": "Clevers",
        "given": "Hans"
      },
      {
        "family": "Deplancke",
        "given": "Bart"
      },
      {
        "family": "Dunham",
        "given": "Ian"
      },
      {
        "family": "Eberwine",
        "given": "James"
      },
      {
        "family": "Eils",
        "given": "Roland"
      },
      {
        "family": "Enard",
        "given": "Wolfgang"
      },
      {
        "family": "Farmer",
        "given": "Andrew"
      },
      {
        "family": "Fugger",
        "given": "Lars"
      },
      {
        "family": "Göttgens",
        "given": "Berthold"
      },
      {
        "family": "Hacohen",
        "given": "Nir"
      },
      {
        "family": "Haniffa",
        "given": "Muzlifah"
      },
      {
        "family": "Hemberg",
        "given": "Martin"
      },
      {
        "family": "Kim",
        "given": "Seung"
      },
      {
        "family": "Klenerman",
        "given": "Paul"
      },
      {
        "family": "Kriegstein",
        "given": "Arnold"
      },
      {
        "family": "Lein",
        "given": "Ed"
      },
      {
        "family": "Linnarsson",
        "given": "Sten"
      },
      {
        "family": "Lundberg",
        "given": "Emma"
      },
      {
        "family": "Lundeberg",
        "given": "Joakim"
      },
      {
        "family": "Majumder",
        "given": "Partha"
      },
      {
        "family": "Marioni",
        "given": "John C"
      },
      {
        "family": "Merad",
        "given": "Miriam"
      },
      {
        "family": "Mhlanga",
        "given": "Musa"
      },
      {
        "family": "Nawijn",
        "given": "Martijn"
      },
      {
        "family": "Netea",
        "given": "Mihai"
      },
      {
        "family": "Nolan",
        "given": "Garry"
      },
      {
        "family": "Pe'er",
        "given": "Dana"
      },
      {
        "family": "Phillipakis",
        "given": "Anthony"
      },
      {
        "family": "Ponting",
        "given": "Chris P"
      },
      {
        "family": "Quake",
        "given": "Stephen"
      },
      {
        "family": "Reik",
        "given": "Wolf"
      },
      {
        "family": "Rozenblatt-Rosen",
        "given": "Orit"
      },
      {
        "family": "Sanes",
        "given": "Joshua"
      },
      {
        "family": "Satija",
        "given": "Rahul"
      },
      {
        "family": "Schumacher",
        "given": "Ton N"
      },
      {
        "family": "Shalek",
        "given": "Alex"
      },
      {
        "family": "Shapiro",
        "given": "Ehud"
      },
      {
        "family": "Sharma",
        "given": "Padmanee"
      },
      {
        "family": "Shin",
        "given": "Jay W"
      },
      {
        "family": "Stegle",
        "given": "Oliver"
      },
      {
        "family": "Stratton",
        "given": "Michael"
      },
      {
        "family": "Stubbington",
        "given": "Michael J T"
      },
      {
        "family": "Theis",
        "given": "Fabian J"
      },
      {
        "family": "Uhlen",
        "given": "Matthias"
      },
      {
        "family": "van Oudenaarden",
        "given": "Alexander"
      },
      {
        "family": "Wagner",
        "given": "Allon"
      },
      {
        "family": "Watt",
        "given": "Fiona"
      },
      {
        "family": "Weissman",
        "given": "Jonathan"
      },
      {
        "family": "Wold",
        "given": "Barbara"
      },
      {
        "family": "Xavier",
        "given": "Ramnik"
      },
      {
        "family": "Yosef",
        "given": "Nir"
      },
      {
        "literal": "Human Cell Atlas Meeting Participants"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2017,
          12,
          5
        ]
      ]
    },
    "abstract": "The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.",
    "container-title": "eLife",
    "DOI": "10.7554/elife.27041",
    "volume": "6",
    "publisher": "eLife Sciences Publications, Ltd",
    "title": "The Human Cell Atlas",
    "URL": "https://doi.org/gcnzcv",
    "PMCID": "PMC5762154",
    "PMID": "29206104",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: doi:10.7554/elife.27041"
  },
  {
    "source": "PubMed",
    "accessed": {
      "date-parts": [
        [
          2020,
          2,
          29
        ]
      ]
    },
    "id": "RoOhUFKU",
    "title": "Secondary Use of EHR: Data Quality Issues and Informatics Opportunities",
    "author": [
      {
        "family": "Botsis",
        "given": "Taxiarchis"
      },
      {
        "family": "Hartvigsen",
        "given": "Gunnar"
      },
      {
        "family": "Chen",
        "given": "Fei"
      },
      {
        "family": "Weng",
        "given": "Chunhua"
      }
    ],
    "container-title-short": "Summit Transl Bioinform",
    "container-title": "Summit on translational bioinformatics",
    "publisher": "American Medical Informatics Association",
    "ISSN": "2153-6430",
    "issued": {
      "date-parts": [
        [
          2010,
          3,
          1
        ]
      ]
    },
    "page": "1-5",
    "volume": "2010",
    "PMID": "21347133",
    "PMCID": "PMC3041534",
    "type": "article-journal",
    "URL": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041534/",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: pmcid:PMC3041534"
  },
  {
    "source": "PubMed",
    "accessed": {
      "date-parts": [
        [
          2020,
          2,
          29
        ]
      ]
    },
    "id": "7s3dpUrT",
    "title": "Impact of electronic health record systems on information integrity: quality and safety implications",
    "author": [
      {
        "family": "Bowman",
        "given": "Sue"
      }
    ],
    "container-title-short": "Perspect Health Inf Manag",
    "container-title": "Perspectives in health information management",
    "publisher": "American Health Information Management Association",
    "ISSN": "1559-4122",
    "issued": {
      "date-parts": [
        [
          2013,
          10,
          1
        ]
      ]
    },
    "page": "1c",
    "volume": "10",
    "issue": "Fall",
    "PMID": "24159271",
    "PMCID": "PMC3797550",
    "type": "article-journal",
    "URL": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797550/",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: pmcid:PMC3797550"
  },
  {
    "source": "PubMed",
    "accessed": {
      "date-parts": [
        [
          2020,
          2,
          29
        ]
      ]
    },
    "id": "1GRT18Tt2",
    "title": "Causal Phenotype Discovery via Deep Networks",
    "author": [
      {
        "family": "Kale",
        "given": "David C"
      },
      {
        "family": "Che",
        "given": "Zhengping"
      },
      {
        "family": "Bahadori",
        "given": "Mohammad Taha"
      },
      {
        "family": "Li",
        "given": "Wenzhe"
      },
      {
        "family": "Liu",
        "given": "Yan"
      },
      {
        "family": "Wetzel",
        "given": "Randall"
      }
    ],
    "container-title-short": "AMIA Annu Symp Proc",
    "container-title": "AMIA ... Annual Symposium proceedings. AMIA Symposium",
    "publisher": "American Medical Informatics Association",
    "ISSN": "1559-4076",
    "issued": {
      "date-parts": [
        [
          2015,
          11,
          5
        ]
      ]
    },
    "page": "677-86",
    "volume": "2015",
    "PMID": "26958203",
    "PMCID": "PMC4765623",
    "type": "article-journal",
    "URL": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4765623/",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: pmcid:PMC4765623"
  },
  {
    "source": "PubMed",
    "accessed": {
      "date-parts": [
        [
          2020,
          2,
          29
        ]
      ]
    },
    "id": "eAP2kxzn",
    "title": "Why Patient Matching Is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields",
    "author": [
      {
        "family": "Just",
        "given": "Beth Haenke"
      },
      {
        "family": "Marc",
        "given": "David"
      },
      {
        "family": "Munns",
        "given": "Megan"
      },
      {
        "family": "Sandefer",
        "given": "Ryan"
      }
    ],
    "container-title-short": "Perspect Health Inf Manag",
    "container-title": "Perspectives in health information management",
    "publisher": "American Health Information Management Association",
    "ISSN": "1559-4122",
    "issued": {
      "date-parts": [
        [
          2016,
          4,
          1
        ]
      ]
    },
    "page": "1e",
    "volume": "13",
    "issue": "Spring",
    "PMID": "27134610",
    "PMCID": "PMC4832129",
    "type": "article-journal",
    "URL": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4832129/",
    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: pmcid:PMC4832129"
  },
  {
    "source": "PubMed",
    "accessed": {
      "date-parts": [
        [
          2020,
          2,
          29
        ]
      ]
    },
    "id": "ybP8QCqL",
    "title": "Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms",
    "author": [
      {
        "family": "De Sa",
        "given": "Christopher"
      },
      {
        "family": "Zhang",
        "given": "Ce"
      },
      {
        "family": "Olukotun",
        "given": "Kunle"
      },
      {
        "family": "Ré",
        "given": "Christopher"
      }
    ],
    "container-title-short": "Adv Neural Inf Process Syst",
    "container-title": "Advances in neural information processing systems",
    "ISSN": "1049-5258",
    "issued": {
      "date-parts": [
        [
          2015,
          12
        ]
      ]
    },
    "page": "2656-2664",
    "volume": "28",
    "PMID": "27330264",
    "PMCID": "PMC4907892",
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    "title": "Random Search for Hyper-Parameter Optimization",
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    "title": "Visualizing Data using t-SNE",
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    "title": "The most interesting case of scientific irreproducibility?",
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    "abstract": "Extracting adverse drug events receives much research attention in the biomedical community. Previous work adopts pipeline models, firstly recognizing drug/disease entity mentions and then identifying adverse drug events from drug/disease pairs. In this paper, we investigate joint models for simultaneously extracting drugs, diseases and adverse drug events. Compared with pipeline models, joint models have two main advantages. First, they make use of information integration to facilitate performance improvement; second, they reduce error propagation in pipeline methods. We compare a discrete model and a deep neural model for extracting drugs, diseases and adverse drug events jointly. Experimental results on a standard ADE corpus show that the discrete joint model outperforms a state-of-the-art baseline pipeline significantly. In addition, when discrete features are replaced by neural features, the recall is further improved.",
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    "abstract": "We design a novel, communication-efficient, failure-robust protocol for secure aggregation of high-dimensional data. Our protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner (i.e. without learning each user's individual contribution), and can be used, for example, in a federated learning setting, to aggregate user-provided model updates for a deep neural network. We prove the security of our protocol in the honest-but-curious and malicious settings, and show that security is maintained even if an arbitrarily chosen subset of users drop out at any time. We evaluate the efficiency of our protocol and show, by complexity analysis and a concrete implementation, that its runtime and communication overhead remain low even on large data sets and client pools. For 16-bit input values, our protocol offerscommunication expansion forusers and-dimensional vectors, andexpansion for\n\nusers and-dimensional vectors over sending data in the clear.",
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    "number": "281",
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    "title": "“Data is the New Oil” — A Ludicrous Proposition",
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    "author": [
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        "family": "Haupt",
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    "type": "article-journal",
    "title": "Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data",
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    "abstract": "Some machine learning applications involve training data that is sensitive, such\n  as the medical histories of patients in a clinical trial. A model may\n  inadvertently and implicitly store some of its...",
    "URL": "https://openreview.net/forum?id=HkwoSDPgg",
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        "family": "Papernot",
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        "given": "Lfar"
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        "family": "Goodfellow",
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        "family": "Talwar",
        "given": "Kunal"
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    "title": "Diet Networks: Thin Parameters for Fat Genomics",
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    "URL": "https://openreview.net/forum?id=Sk-oDY9ge&noteId=Sk-oDY9ge",
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    "journalAbbreviation": "ICLR 2017",
    "author": [
      {
        "family": "Romero",
        "given": "Adriana"
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      {
        "family": "Carrier",
        "given": "Pierre Luc"
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      {
        "family": "Erraqabi",
        "given": "Akram"
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      {
        "family": "Sylvain",
        "given": "Tristan"
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      {
        "family": "Auvolat",
        "given": "Alex"
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      {
        "family": "Dejoie",
        "given": "Etienne"
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      {
        "family": "Legault",
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    "title": "Algorithms for Hyper-parameter Optimization",
    "container-title": "Proceedings of the 24th International Conference on Neural Information Processing Systems",
    "collection-title": "NIPS'11",
    "publisher": "Curran Associates Inc.",
    "publisher-place": "USA",
    "page": "2546–2554",
    "source": "ACM Digital Library",
    "event-place": "USA",
    "abstract": "Several recent advances to the state of the art in image classification benchmarks have come from better configurations of existing techniques rather than novel approaches to feature learning. Traditionally, hyper-parameter optimization has been the job of humans because they can be very efficient in regimes where only a few trials are possible. Presently, computer clusters and GPU processors make it possible to run more trials and we show that algorithmic approaches can find better results. We present hyper-parameter optimization results on tasks of training neural networks and deep belief networks (DBNs). We optimize hyper-parameters using random search and two new greedy sequential methods based on the expected improvement criterion. Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreliable for training DBNs. The sequential algorithms are applied to the most difficult DBN learning problems from [1] and find significantly better results than the best previously reported. This work contributes novel techniques for making response surface models P(y|x) in which many elements of hyper-parameter assignment (x) are known to be irrelevant given particular values of other elements.",
    "URL": "http://dl.acm.org/citation.cfm?id=2986459.2986743",
    "ISBN": "978-1-61839-599-3",
    "author": [
      {
        "family": "Bergstra",
        "given": "James"
      },
      {
        "family": "Bardenet",
        "given": "Rémi"
      },
      {
        "family": "Bengio",
        "given": "Yoshua"
      },
      {
        "family": "Kégl",
        "given": "Balázs"
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      ]
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  {
    "id": "CCS5KSIM",
    "type": "paper-conference",
    "title": "ImageNet Classification with Deep Convolutional Neural Networks",
    "container-title": "Proceedings of the 25th International Conference on Neural Information Processing Systems",
    "collection-title": "NIPS'12",
    "publisher": "Curran Associates Inc.",
    "publisher-place": "USA",
    "page": "1097–1105",
    "source": "ACM Digital Library",
    "event-place": "USA",
    "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.",
    "URL": "http://dl.acm.org/citation.cfm?id=2999134.2999257",
    "author": [
      {
        "family": "Krizhevsky",
        "given": "Alex"
      },
      {
        "family": "Sutskever",
        "given": "Ilya"
      },
      {
        "family": "Hinton",
        "given": "Geoffrey E."
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    "issued": {
      "date-parts": [
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          "2012"
        ]
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  },
  {
    "id": "enhj7VT6",
    "type": "chapter",
    "title": "How transferable are features in deep neural networks?",
    "container-title": "Advances in Neural Information Processing Systems 27",
    "publisher": "Curran Associates, Inc.",
    "page": "3320–3328",
    "source": "Neural Information Processing Systems",
    "URL": "http://papers.nips.cc/paper/5347-how-transferable-are-features-in-deep-neural-networks.pdf",
    "author": [
      {
        "family": "Yosinski",
        "given": "Jason"
      },
      {
        "family": "Clune",
        "given": "Jeff"
      },
      {
        "family": "Bengio",
        "given": "Yoshua"
      },
      {
        "family": "Lipson",
        "given": "Hod"
      }
    ],
    "editor": [
      {
        "family": "Ghahramani",
        "given": "Z."
      },
      {
        "family": "Welling",
        "given": "M."
      },
      {
        "family": "Cortes",
        "given": "C."
      },
      {
        "family": "Lawrence",
        "given": "N. D."
      },
      {
        "family": "Weinberger",
        "given": "K. Q."
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      "date-parts": [
        [
          "2014"
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      ]
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    "type": "webpage",
    "title": "Implementations by Phenotype | PheKB",
    "URL": "https://phekb.org/implementations",
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    "type": "report",
    "title": "Globoko ucenje na genomskih in filogenetskih podatkih",
    "publisher": "Univerza v Ljubljani, Fakulteta za racunalništvo in informatiko",
    "source": "repozitorij.uni-lj.si",
    "URL": "https://repozitorij.uni-lj.si/IzpisGradiva.php?id=85515",
    "language": "slv",
    "author": [
      {
        "family": "Mrzelj",
        "given": "Nina"
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      "date-parts": [
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          "2016"
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      "date-parts": [
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          "2017",
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    "type": "webpage",
    "title": "Improving the speed of neural networks on CPUs",
    "container-title": "Google Research",
    "URL": "https://research.google/pubs/pub37631/",
    "language": "en",
    "author": [
      {
        "family": "Vanhoucke",
        "given": "Vincent"
      },
      {
        "family": "Senior",
        "given": "Andrew"
      },
      {
        "family": "Mao",
        "given": "Mark Z."
      }
    ],
    "issued": {
      "date-parts": [
        [
          "2011"
        ]
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    "accessed": {
      "date-parts": [
        [
          "2020",
          2,
          29
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    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: url:https://research.google.com/pubs/pub37631.html"
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    "id": "6MR50hyY",
    "type": "post-weblog",
    "title": "Proof of prespecified endpoints in medical research with the bitcoin blockchain – The Grey Literature",
    "URL": "https://blog.bgcarlisle.com/2014/08/25/proof-of-prespecified-endpoints-in-medical-research-with-the-bitcoin-blockchain/",
    "language": "en-US",
    "accessed": {
      "date-parts": [
        [
          "2020",
          2,
          29
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    "note": "This CSL JSON Item was automatically generated by Manubot v0.3.1 using citation-by-identifier.\nstandard_id: url:https://www.bgcarlisle.com/blog/2014/08/25/proof-of-prespecified-endpoints-in-medical-research-with-the-bitcoin-blockchain/"
  },
  {
    "URL": "https://www.cameo3d.org/",
    "title": "CAMEO - Continuous Automated Model Evaluation",
    "issued": {
      "date-parts": [
        [
          2017
        ]
      ]
    },
    "type": "webpage",
    "id": "5OOI0sS2",
    "note": "This CSL JSON Item was loaded by Manubot v0.3.1 from a manual reference file.\nmanual_reference_filename: manual-references.json\nstandard_id: url:https://www.cameo3d.org"
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  {
    "id": "IcdRxiDv",
    "type": "report",
    "title": "Learning multiple layers of features from tiny images",
    "author": [
      {
        "family": "Krizhevsky",
        "given": "Alex"
      }
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    "issued": {
      "date-parts": [
        [
          "2009"
        ]
      ]
    },
    "URL": "https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf",
    "note": "This CSL JSON Item was loaded by Manubot v0.3.1 from a manual reference file.\nmanual_reference_filename: manual-references.json\nstandard_id: url:https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf"
  },
  {
    "id": "waowNTz7",
    "type": "article-journal",
    "title": "Utilizing Machine Learning Approaches to Understand the Interrelationship of Diet, the Human Gastrointestinal Microbiome, and Health",
    "container-title": "The FASEB Journal",
    "page": "406.3-406.3",
    "volume": "30",
    "issue": "1_supplement",
    "source": "fasebj.org (Atypon)",
    "abstract": "BackgroundA growing body of literature supports the ability of specific foods and nutrients to impact the gastrointestinal microbiome. However, there is a dearth of knowledge on the interplay of dietary components (e.g. foods and nutrients), gastrointestinal bacteria, and bacterial metabolites. Current analytical approaches limit investigation of these complex interrelations; therefore, further research utilizing modern machine learning methods is needed.ObjectiveWe aimed to fill the gap in knowledge about the interrelationship of diet, the gastrointestinal microbiome, and health by utilizing multivariate approaches that address P>>N, many features but few samples to 1) validate results generated by prototype software with previously published results; and 2) identify novel associations among relevant foods, nutrients, bacteria, and bacterial metabolites.MethodsData generated from a human dietary intervention study that included habitual food and nutrient intake patterns (NHANES food frequency questionnaire), daily dietary intake records, breath gas, and fecal bacteria and metabolite data were analyzed using machine learning approaches. Relevant bacteria, bacterial metabolites, foods, and nutrients were identified using Random Forest and relationships among these relevant features were determined using Maximal Information Coefficient.ResultsBreath hydrogen and agave inulin supplementation were predictive of Bifidobacterium abundance. Habitual diet factors were highly relevant in predicting participant body mass index (BMI). Among bacteria, Phascolarctobacterium, Collinsella, and Erysipelotrichacea were important features for predicting BMI. Oscillospira was associated with habitual dietary glycemic load. Phascolarctobacterium and Eubacterium were associated with habitual intake of deep yellow vegetables. Dietary polyunstaruated fatty acids were relevant in predicting Bacteroidetes to Firmicutes ratios. Total fiber and butyrate were highly relevant in predicting Faecalibacterium abundance.ConclusionsFindings revealed similar associations among diet, bacterial genera, and breath hydrogen measures as previously published results, thereby validating the use of these methods in diet-microbiome studies. BMI, bacterial abundances, dietary fiber intake, glycemic load, and deep yellow vegetable intake were interrelated. As features of a high quality diet were relevant for predicting bacterial abundances and metabolites, additional work is ongoing to calculate healthy eating index scores. These scores will help delineate purported health benefits (or detriments) of dietary components, bacteria, and bacterial metabolites and aid in identifying targets for future diet-microbiome studies.Support or Funding InformationThis study was partially funded by the Illinois Applied Research Institute.",
    "URL": "https://www.fasebj.org/doi/abs/10.1096/fasebj.30.1_supplement.406.3",
    "ISSN": "0892-6638",
    "journalAbbreviation": "The FASEB Journal",
    "author": [
      {
        "family": "Guetterman",
        "given": "Heather"
      },
      {
        "family": "Auvil",
        "given": "Loretta"
      },
      {
        "family": "Russell",
        "given": "Nate"
      },
      {
        "family": "Welge",
        "given": "Michael"
      },
      {
        "family": "Berry",
        "given": "Matt"
      },
      {
        "family": "Gatzke",
        "given": "Lisa"
      },
      {
        "family": "Bushell",
        "given": "Colleen"
      },
      {
        "family": "Holscher",
        "given": "Hannah"
      }
    ],
    "issued": {
      "date-parts": [
        [
          2016,
          4,
          1
        ]
      ]
    },
    "note": "This CSL JSON Item was loaded by Manubot v0.3.1 from a manual reference file.\nmanual_reference_filename: manual-references.json\nstandard_id: url:https://www.fasebj.org/doi/abs/10.1096/fasebj.30.1_supplement.406.3"
  },
  {
    "URL": "https://www.nigms.nih.gov/Education/Documents/curiosity.pdf",
    "title": "Curiosity Creates Cures: The Value and Impact of Basic Research",
    "issued": {
      "date-parts": [
        [
          2012,
          5
        ]
      ]
    },
    "author": [
      {
        "family": "NIH"
      }
    ],
    "type": "webpage",
    "id": "ru0hjGeQ",
    "note": "This CSL JSON Item was loaded by Manubot v0.3.1 from a manual reference file.\nmanual_reference_filename: manual-references.json\nstandard_id: url:https://www.nigms.nih.gov/Education/Documents/curiosity.pdf"
  },
  {
    "URL": "https://www.synapse.org/#!Synapse:syn6131484/wiki/402026",
    "title": "ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge",
    "issued": {
      "date-parts": [
        [
          2017
        ]
      ]
    },
    "type": "webpage",
    "id": "wW6QbBXz",
    "note": "This CSL JSON Item was loaded by Manubot v0.3.1 from a manual reference file.\nmanual_reference_filename: manual-references.json\nstandard_id: url:https://www.synapse.org/#!Synapse:syn6131484/wiki/402026"
  }
]