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author | Rémi Flamary <remi.flamary@gmail.com> | 2018-09-24 15:34:51 +0200 |
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committer | Rémi Flamary <remi.flamary@gmail.com> | 2018-09-24 15:34:51 +0200 |
commit | 67c98a68cc114335a8ea48106bb7fd3c8a57f831 (patch) | |
tree | 2d1852afc1ae83e23c63c68863c9bbbed5ff0847 /docs/source/readme.rst | |
parent | ca08b788af38a076f45f000003eb0e2f227d7fd5 (diff) | |
parent | 22d310d554239a854a5027397a5a6dff7cfe8d3c (diff) |
merge doc
Diffstat (limited to 'docs/source/readme.rst')
-rw-r--r-- | docs/source/readme.rst | 33 |
1 files changed, 24 insertions, 9 deletions
diff --git a/docs/source/readme.rst b/docs/source/readme.rst index d10b769..e7c2bd1 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -2,7 +2,7 @@ POT: Python Optimal Transport ============================= |PyPI version| |Anaconda Cloud| |Build Status| |Documentation Status| -|Anaconda downloads| |License| +|Downloads| |Anaconda downloads| |License| This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and @@ -13,14 +13,14 @@ 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] with optional GPU implementation - (requires cudamat). + and stabilized version [9][10] and greedy SInkhorn [22] with optional + GPU implementation (requires cudamat). - Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. - Non regularized Wasserstein barycenters [16] with LP solver (only small scale). -- Non regularized free support Wasserstein barycenters [20]. -- Bregman projections for Wasserstein barycenter [3] and unmixing [4]. +- Bregman projections for Wasserstein barycenter [3], convolutional + barycenter [21] and unmixing [4]. - Optimal transport for domain adaptation with group lasso regularization [5] - Conditional gradient [6] and Generalized conditional gradient for @@ -32,6 +32,7 @@ It provides the following solvers: [12]) - Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) +- Non regularized free support Wasserstein barycenters [20]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -107,7 +108,7 @@ Dependencies Some sub-modules require additional dependences which are discussed below -- **ot.dr** (Wasserstein dimensionality rediuction) depends on autograd +- **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: :: @@ -222,8 +223,9 @@ The contributors to this library are: - `Stanislas Chambon <https://slasnista.github.io/>`__ - `Antoine Rolet <https://arolet.github.io/>`__ - Erwan Vautier (Gromov-Wasserstein) -- `Kilian Fatras <https://kilianfatras.github.io/>`__ (Stochastic - optimization) +- `Kilian Fatras <https://kilianfatras.github.io/>`__ +- `Alain + Rakotomamonjy <https://sites.google.com/site/alainrakotomamonjy/home>`__ This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -341,7 +343,7 @@ Statistics (AISTATS). [18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) `Stochastic Optimization for Large-scale Optimal -Transport <arXiv%20preprint%20arxiv:1605.08527>`__. Advances in Neural +Transport <https://arxiv.org/abs/1605.08527>`__. Advances in Neural Information Processing Systems (2016). [19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, @@ -353,6 +355,17 @@ Conference on Learning Representation (2018) Barycenters <http://proceedings.mlr.press/v32/cuturi14.html>`__. International Conference in Machine Learning +[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., +Nguyen, A. & Guibas, L. (2015). `Convolutional wasserstein distances: +Efficient optimal transportation on geometric +domains <https://dl.acm.org/citation.cfm?id=2766963>`__. ACM +Transactions on Graphics (TOG), 34(4), 66. + +[22] J. Altschuler, J.Weed, P. Rigollet, (2017) `Near-linear time +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 + .. |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 @@ -361,6 +374,8 @@ International Conference in Machine Learning :target: https://travis-ci.org/rflamary/POT .. |Documentation Status| image:: https://readthedocs.org/projects/pot/badge/?version=latest :target: http://pot.readthedocs.io/en/latest/?badge=latest +.. |Downloads| image:: https://pepy.tech/badge/pot + :target: https://pepy.tech/project/pot .. |Anaconda downloads| image:: https://anaconda.org/conda-forge/pot/badges/downloads.svg :target: https://anaconda.org/conda-forge/pot .. |License| image:: https://anaconda.org/conda-forge/pot/badges/license.svg |