@@ -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
@@ -199,7 +201,6 @@ languages):
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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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+
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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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