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authorRémi Flamary <remi.flamary@gmail.com>2017-09-14 16:57:29 +0200
committerRémi Flamary <remi.flamary@gmail.com>2017-09-14 16:57:29 +0200
commitd27e14cb4a48e76ab132c53aca56155e9be50bda (patch)
tree3ff596deaea310266ed879aa4adee3886c51a3ea /docs/source/readme.rst
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@@ -21,6 +21,7 @@ It provides the following solvers:
- Joint OT matrix and mapping estimation [8].
- Wasserstein Discriminant Analysis [11] (requires autograd +
pymanopt).
+- Gromov-Wasserstein distances and barycenters [12]
Some demonstrations (both in Python and Jupyter Notebook format) are
available in the examples folder.
@@ -190,6 +191,7 @@ The contributors to this library are:
- `Nathalie
Gayraud <https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1>`__
- `Stanislas Chambon <https://slasnista.github.io/>`__
+- `Antoine Rolet <https://arolet.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
@@ -199,7 +201,6 @@ languages):
in Matlab)
- `Nicolas Bonneel <http://liris.cnrs.fr/~nbonneel/>`__ ( C++ code for
EMD)
-- `Antoine Rolet <https://arolet.github.io/>`__ ( Mex file for EMD )
- `Marco Cuturi <http://marcocuturi.net/>`__ (Sinkhorn Knopp in
Matlab/Cuda)
@@ -280,6 +281,11 @@ arXiv:1607.05816.
Analysis <https://arxiv.org/pdf/1608.08063.pdf>`__. arXiv preprint
arXiv:1608.08063.
+[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon,
+`Gromov-Wasserstein averaging of kernel and distance
+matrices <http://proceedings.mlr.press/v48/peyre16.html>`__
+International Conference on Machine Learning (ICML). 2016.
+
.. |PyPI version| image:: https://badge.fury.io/py/POT.svg
:target: https://badge.fury.io/py/POT
.. |Build Status| image:: https://travis-ci.org/rflamary/POT.svg?branch=master