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author | Nicolas Courty <Nico@pc-mna-08.univ-ubs.fr> | 2017-08-28 14:41:09 +0200 |
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committer | Nicolas Courty <Nico@MacBook-Pro-de-Nicolas.local> | 2017-09-01 11:09:13 +0200 |
commit | 24362ecde2a64353e568d3980a52ea5ddfdbe930 (patch) | |
tree | a2412bdfe129a46b54f606dd62dce4c318bb8fa0 /README.md | |
parent | 59640017434427bac54d9eb668a1d7fc862ccdce (diff) |
Gromov-Wasserstein distance
Diffstat (limited to 'README.md')
-rw-r--r-- | README.md | 4 |
1 files changed, 3 insertions, 1 deletions
@@ -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 [12] Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -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] Peyré, Gabriel, 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. |