Boosting VI NN

Boosting VI NN is a machine learning project that combines boosting techniques with neural network architectures for improved variational inference performance. This repository contains the code, experiments, and documentation to help you get started with the project.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Examples
• Contributing
• License
• Contact

Overview

Boosting VI NN is designed to leverage boosting methods to enhance the predictive performance of variational inference neural networks. By iteratively refining weak learners and combining their outputs, the framework aims to:

• Improve model accuracy.
• Handle complex data distributions.
• Offer robust uncertainty quantification.

This repository includes:

• The core boosting algorithm implementation.
• Example neural network architectures.
• Scripts for training and evaluation.
• Experiment logs and results for reproducibility.

Features

• Boosting Framework: Implementation of a boosting mechanism tailored for neural networks.
• Variational Inference: Efficient techniques for approximating complex posterior distributions.
• Modular Design: Easily extendable components for experimenting with different neural architectures.
• Reproducibility: Scripts and configuration files to reproduce the experiments.

Installation

To install the required dependencies, follow these steps:

1. Clone the Repository:

   git clone https://github.com/yourusername/boosting-vi-nn.git
   cd boosting-vi-nn
   

2. Create and Activate a Virtual Environment (optional but recommended):

   python3 -m venv env
   # Activate the virtual environment:
   # For Linux/macOS:
   source env/bin/activate
   # For Windows (uncomment the following line and comment out the above if needed):
   # env\Scripts\activate##
   

3. Install the Dependencies:

   pip install -r requirements.txt
   

4. Set Up the Configuration:

   • Customize the configuration file located in the config/ directory if necessary.
   • The default configuration is provided in```bash config/default.yaml.

## Usage

Once the installation is complete, you can start using **Boosting VI NN**. Below are some common commands:

### Training the Model:
```bash
python train.py --config config/default.yaml

Evaluating the Model:

python evaluate.py --config config/default.yaml

Visualizing Results:

python visualize.py --config config/default.yaml

For more detailed instructions and parameter explanations, refer to the documentation in the docs/ directory.

Examples

For detailed examples and tutorials, please check out the examples provided:

• Basic Example: examples/basic_example.ipynb
• Advanced Configuration: examples/advanced_config.ipynb

These notebooks demonstrate how to train, evaluate, and customize the Boosting VI NN framework for your projects.

Contributing

Contributions to Boosting VI NN are welcome! To contribute:

• Fork the repository.
• Create a new branch for your feature or bug fix.
• Make sure your code adheres to the project’s coding standards and add tests where appropriate.
• Submit a pull request detailing your changes.

License

Boosting VI NN is released under the MIT License. See the LICENSE file for more details.

Contact

If you have any questions, suggestions, or need further assistance, please feel free to reach out:

• GitHub: paviabera
• Email: paviabera@usf.edu