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Convolutional wasserstein distances: efficient optimal transportation on geometric domains.

, , , , , , , and . ACM Trans. Graph., 34 (4): 66:1-66:11 (2015)

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Stochastic Deep Networks, , and . (2018)cite arxiv:1811.07429.Learning Generative Models with Sinkhorn Divergences., , and . AISTATS, volume 84 of Proceedings of Machine Learning Research, page 1608-1617. PMLR, (2018)Differentiable Sorting using Optimal Transport: The Sinkhorn CDF and Quantile Operator., , and . CoRR, (2019)Mean Reversion with a Variance Threshold., and . ICML (3), volume 28 of JMLR Workshop and Conference Proceedings, page 271-279. JMLR.org, (2013)Fast Computation of Wasserstein Barycenters., and . ICML, volume 32 of JMLR Workshop and Conference Proceedings, page 685-693. JMLR.org, (2014)Sample Complexity of Sinkhorn Divergences, , , , and . Proceedings of Machine Learning Research, volume 89 of Proceedings of Machine Learning Research, page 1574--1583. PMLR, (16--18 Apr 2019)Wasserstein regularization for sparse multi-task regression., , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1407-1416. PMLR, (2019)Geodesic PCA versus Log-PCA of Histograms in the Wasserstein Space., , , , and . SIAM J. Sci. Comput., (2018)Rethinking Initialization of the Sinkhorn Algorithm., and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 8682-8698. PMLR, (2023)Low-Rank Sinkhorn Factorization., , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 9344-9354. PMLR, (2021)