CircleCI update of dev docs (3319).

Author: Circle CI

master/_downloads/06f3dd7c3258690ab730be0a658b5e36/plot_ssw_unif_torch.zip

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-        "\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"
+        "\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., & Carin, L. (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"
       ]
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