From 2d7db0ed112b9349dc0b0c4cc7a9f3ea8da4ebed Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Jun 2019 15:01:13 +0200 Subject: update readme --- docs/source/readme.rst | 14 ++++++++++++++ 1 file changed, 14 insertions(+) (limited to 'docs') diff --git a/docs/source/readme.rst b/docs/source/readme.rst index b7828d3..320ddd5 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -35,6 +35,7 @@ It provides the following solvers: - 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. @@ -69,6 +70,13 @@ modules: 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: :: @@ -229,6 +237,8 @@ The contributors to this library are - `Alain Rakotomamonjy `__ - `Vayer Titouan `__ +- `Hicham Janati `__ (Unbalanced OT) +- `Romain Tavenard `__ (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 @@ -379,6 +389,10 @@ and Statistics, (AISTATS) 21, 2018 graphs `__ 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 `__ +Advances in Neural Information Processing Systems (NIPS). + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg -- cgit v1.2.3