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author | Kilian Fatras <kilianfatras@Kilians-MacBook-Air.local> | 2019-03-29 12:41:43 +0100 |
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committer | Kilian Fatras <kilianfatras@Kilians-MacBook-Air.local> | 2019-03-29 12:41:43 +0100 |
commit | a2545b5a503c95c9bf07948929b77e9c3f4f28d3 (patch) | |
tree | 84bc0c169c1121bdff56e77c2c6cc88a68efba67 /README.md | |
parent | 2384380536e3cc405e4db9f4b31cb48d309f257c (diff) |
add empirical sinkhorn and sikhorn divergence functions
Diffstat (limited to 'README.md')
-rw-r--r-- | README.md | 2 |
1 files changed, 2 insertions, 0 deletions
@@ -230,3 +230,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https://dl.acm.org/citation.cfm?id=2766963). ACM Transactions on Graphics (TOG), 34(4), 66. [22] J. Altschuler, J.Weed, P. Rigollet, (2017) [Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration](https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf), Advances in Neural Information Processing Systems (NIPS) 31 + +[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 |