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| author | Rémi Flamary <remi.flamary@gmail.com> | 2019-06-06 12:09:34 +0200 |
|---|---|---|
| committer | Rémi Flamary <remi.flamary@gmail.com> | 2019-06-06 12:09:34 +0200 |
| commit | 0fc6938dc15e8888b0a73fa4b6a421f39f0e0697 (patch) | |
| tree | 8d6af36ae13875362d74a4be05d447a0e6a3d712 /docs/source/readme.rst | |
| parent | 5a6b226de20624b51c2ff98bc30e5611a7a788c7 (diff) | |
update conf + readme
Diffstat (limited to 'docs/source/readme.rst')
| -rw-r--r-- | docs/source/readme.rst | 30 |
1 files changed, 19 insertions, 11 deletions
diff --git a/docs/source/readme.rst b/docs/source/readme.rst index e7c2bd1..d1063e8 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -12,9 +12,11 @@ It provides the following solvers: - OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -- Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] - and stabilized version [9][10] and greedy SInkhorn [22] with optional - GPU implementation (requires cudamat). +- Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], + stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU + implementation (requires cupy). +- Sinkhorn divergence [23] and entropic regularization OT from + empirical data. - Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. - Non regularized Wasserstein barycenters [16] with LP solver (only @@ -115,14 +117,9 @@ below pip install pymanopt autograd -- **ot.gpu** (GPU accelerated OT) depends on cudamat that have to be - installed with: - - :: - - git clone https://github.com/cudamat/cudamat.git - cd cudamat - python setup.py install --user # for user install (no root) +- **ot.gpu** (GPU accelerated OT) depends on cupy that have to be + installed following instructions on `this + page <https://docs-cupy.chainer.org/en/stable/install.html>`__. obviously you need CUDA installed and a compatible GPU. @@ -226,6 +223,7 @@ 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/>`__ This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -366,6 +364,16 @@ approximation algorithms for optimal transport via Sinkhorn iteration <https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf>`__, Advances in Neural Information Processing Systems (NIPS) 31 +[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). + .. |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 |