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docs/source/readme.rst

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@@ -21,6 +21,7 @@ It provides the following solvers:
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- Joint OT matrix and mapping estimation [8].
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- Wasserstein Discriminant Analysis [11] (requires autograd +
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pymanopt).
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- Gromov-Wasserstein distances and barycenters [12]
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Some demonstrations (both in Python and Jupyter Notebook format) are
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available in the examples folder.
@@ -190,6 +191,7 @@ The contributors to this library are:
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- `Nathalie
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Gayraud <https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1>`__
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- `Stanislas Chambon <https://slasnista.github.io/>`__
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- `Antoine Rolet <https://arolet.github.io/>`__
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This toolbox benefit a lot from open source research and we would like
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to thank the following persons for providing some code (in various
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in Matlab)
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- `Nicolas Bonneel <http://liris.cnrs.fr/~nbonneel/>`__ ( C++ code for
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EMD)
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- `Antoine Rolet <https://arolet.github.io/>`__ ( Mex file for EMD )
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- `Marco Cuturi <http://marcocuturi.net/>`__ (Sinkhorn Knopp in
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Matlab/Cuda)
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@@ -280,6 +281,11 @@ arXiv:1607.05816.
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Analysis <https://arxiv.org/pdf/1608.08063.pdf>`__. arXiv preprint
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arXiv:1608.08063.
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[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon,
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`Gromov-Wasserstein averaging of kernel and distance
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matrices <http://proceedings.mlr.press/v48/peyre16.html>`__
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International Conference on Machine Learning (ICML). 2016.
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.. |PyPI version| image:: https://badge.fury.io/py/POT.svg
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:target: https://badge.fury.io/py/POT
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.. |Build Status| image:: https://travis-ci.org/rflamary/POT.svg?branch=master

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