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@@ -16,7 +16,7 @@ It provides the following solvers: * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. * 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. @@ -138,12 +138,12 @@ The contributors to this library are: * [Léo Gautheron](https://github.com/aje) (GPU implementation) * [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 languages): * [Gabriel Peyré](http://gpeyre.github.io/) (Wasserstein Barycenters 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) @@ -184,3 +184,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https://arxiv.org/pdf/1607.05816.pdf). arXiv preprint arXiv:1607.05816. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant 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. |