CircleCI update of dev docs (3223).

Author: Circle CI

master/_downloads/006964755fe89c4eeb7c8b8016e96890/plot_otda_semi_supervised.py

@@ -4,6 +4,9 @@
 OTDA unsupervised vs semi-supervised setting
 ============================================
 
+.. note::
+    Example added in release: 0.1.9.
+
 This example introduces a semi supervised domain adaptation in a 2D setting.
 It explicit the problem of semi supervised domain adaptation and introduces
 some optimal transport approaches to solve it.

master/_downloads/020b892b91e910d18254039df8f9f86e/plot_regpath.py

@@ -3,6 +3,10 @@
 ================================================================
 Regularization path of l2-penalized unbalanced optimal transport
 ================================================================
+
+.. note::
+    Example added in release: 0.8.0.
+
 This example illustrate the regularization path for 2D unbalanced
 optimal transport. We present here both the fully relaxed case
 and the semi-relaxed case.

master/_downloads/0228827bdcc2a6735b1faf26c864145d/plot_OT_1D_smooth.zip

@@ -4,6 +4,9 @@
 Screened optimal transport (Screenkhorn)
 ========================================
 
+.. note::
+    Example added in release: 0.7.0.
+
 This example illustrates the computation of Screenkhorn [26].
 
 [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019).

master/_downloads/0598d5b45ff5db99569cf59aaff1ea1a/plot_screenkhorn_1D.py

@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.17"
+      "version": "3.10.18"
     }
   },
   "nbformat": 4,

master/_downloads/059a63fc6cb655dcd2f1c9a61bb9d7ec/plot_OT_2D_samples.ipynb

@@ -4,6 +4,9 @@
 OT for image color adaptation
 =============================
 
+.. note::
+    Example added in release: 0.1.9.
+
 This example presents a way of transferring colors between two images
 with Optimal Transport as introduced in [6]
 

master/_downloads/06f3dd7c3258690ab730be0a658b5e36/plot_ssw_unif_torch.zip

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Comparison of Fused Gromov-Wasserstein solvers\n\nThis example illustrates the computation of FGW for attributed graphs\nusing 4 different solvers to estimate the distance based on Conditional\nGradient [24], Sinkhorn projections [12, 51] and alternated Bregman\nprojections [63, 64].\n\nWe generate two graphs following Stochastic Block Models further endowed with\nnode features and compute their FGW matchings.\n\n[12] Gabriel Peyr\u00e9, Marco Cuturi, and Justin Solomon (2016),\n\"Gromov-Wasserstein averaging of kernel and distance matrices\".\nInternational Conference on Machine Learning (ICML).\n\n[24] Vayer Titouan, Chapel Laetitia, Flamary R\u00e9mi, Tavenard Romain\nand Courty Nicolas\n\"Optimal Transport for structured data with application on graphs\"\nInternational Conference on Machine Learning (ICML). 2019.\n\n[51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019).\n\"Gromov-wasserstein learning for graph matching and node embedding\".\nIn International Conference on Machine Learning (ICML), 2019.\n\n[63] Li, J., Tang, J., Kong, L., Liu, H., Li, J., So, A. M. C., & Blanchet, J.\n\"A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in\nGraph Data\". International Conference on Learning Representations (ICLR), 2023.\n\n[64] Ma, X., Chu, X., Wang, Y., Lin, Y., Zhao, J., Ma, L., & Zhu, W.\n\"Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications\".\nIn Thirty-seventh Conference on Neural Information Processing Systems\n(NeurIPS), 2023.\n"
+        "\n# Comparison of Fused Gromov-Wasserstein solvers\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Example added in release: 0.9.1.</p></div>\n\nThis example illustrates the computation of FGW for attributed graphs\nusing 4 different solvers to estimate the distance based on Conditional\nGradient [24], Sinkhorn projections [12, 51] and alternated Bregman\nprojections [63, 64].\n\nWe generate two graphs following Stochastic Block Models further endowed with\nnode features and compute their FGW matchings.\n\n[12] Gabriel Peyr\u00e9, Marco Cuturi, and Justin Solomon (2016),\n\"Gromov-Wasserstein averaging of kernel and distance matrices\".\nInternational Conference on Machine Learning (ICML).\n\n[24] Vayer Titouan, Chapel Laetitia, Flamary R\u00e9mi, Tavenard Romain\nand Courty Nicolas\n\"Optimal Transport for structured data with application on graphs\"\nInternational Conference on Machine Learning (ICML). 2019.\n\n[51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019).\n\"Gromov-wasserstein learning for graph matching and node embedding\".\nIn International Conference on Machine Learning (ICML), 2019.\n\n[63] Li, J., Tang, J., Kong, L., Liu, H., Li, J., So, A. M. C., & Blanchet, J.\n\"A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in\nGraph Data\". International Conference on Learning Representations (ICLR), 2023.\n\n[64] Ma, X., Chu, X., Wang, Y., Lin, Y., Zhao, J., Ma, L., & Zhu, W.\n\"Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications\".\nIn Thirty-seventh Conference on Neural Information Processing Systems\n(NeurIPS), 2023.\n"
       ]
     },
     {
@@ -89,7 +89,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.17"
+      "version": "3.10.18"
     }
   },
   "nbformat": 4,

master/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Sliced Wasserstein Distance on 2D distributions\n\nThis example illustrates the computation of the sliced Wasserstein Distance as\nproposed in [31].\n\n[31] Bonneel, Nicolas, et al. \"Sliced and radon wasserstein barycenters of\nmeasures.\" Journal of Mathematical Imaging and Vision 51.1 (2015): 22-45\n"
+        "\n# Sliced Wasserstein Distance on 2D distributions\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Example added in release: 0.8.0.</p></div>\n\nThis example illustrates the computation of the sliced Wasserstein Distance as\nproposed in [31].\n\n[31] Bonneel, Nicolas, et al. \"Sliced and radon wasserstein barycenters of\nmeasures.\" Journal of Mathematical Imaging and Vision 51.1 (2015): 22-45\n"
       ]
     },
     {
@@ -118,7 +118,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.17"
+      "version": "3.10.18"
     }
   },
   "nbformat": 4,