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author | Romain Tavenard <romain.tavenard@univ-rennes2.fr> | 2019-06-27 10:54:13 +0200 |
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committer | GitHub <noreply@github.com> | 2019-06-27 10:54:13 +0200 |
commit | bbc56e74bf119b8810c0de7b446bb01b30efc3c2 (patch) | |
tree | 223e26e51da0edd3057fa16820ce7f1882a94f59 /README.md | |
parent | 0d333e004636f5d25edea6bb195e8e4d9a95ba98 (diff) | |
parent | 2364d56aad650d501753cc93a69ea1b8ddf28b0a (diff) |
Merge branch 'master' into master
Diffstat (limited to 'README.md')
-rw-r--r-- | README.md | 4 |
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@@ -27,6 +27,7 @@ It provides the following solvers: * Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) * Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) * Non regularized free support Wasserstein barycenters [20]. +* Unbalanced OT with KL relaxation distance and barycenter [10, 25]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -165,6 +166,7 @@ The contributors to this library are: * [Kilian Fatras](https://kilianfatras.github.io/) * [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) * [Vayer Titouan](https://tvayer.github.io/) +* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): @@ -236,3 +238,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 [24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML). + +[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2019). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). |