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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. @@ -53,6 +54,12 @@ The library has been tested on Linux, MacOSX and Windows. It requires a C++ comp #### Pip installation +Note that due to a limitation of pip, `cython` and `numpy` need to be installed +prior to installing POT. This can be done easily with +``` +pip install numpy cython +``` + You can install the toolbox through PyPI with: ``` pip install POT @@ -62,6 +69,8 @@ or get the very latest version by downloading it and then running: python setup.py install --user # for user install (no root) ``` + + #### Anaconda installation with conda-forge If you use the Anaconda python distribution, POT is available in [conda-forge](https://conda-forge.org). To install it and the required dependencies: @@ -150,7 +159,12 @@ You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter. ## Acknowledgements -The contributors to this library are: +This toolbox has been created and is maintained by + +* [Rémi Flamary](http://remi.flamary.com/) +* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) + +The contributors to this library are * [Rémi Flamary](http://remi.flamary.com/) * [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) @@ -165,6 +179,8 @@ 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) +* [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) 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 +252,5 @@ 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). |