| Sinkhorn Distances: Lightspeed Computation of Optimal Transport M Cuturi Proceedings of the 26th International Conference on Advances in Neural …, 2013 | 2188 | 2013 |
| Computational Optimal Transport G Peyré, M Cuturi Foundations and Trends in Machine Learning 11 (5-6), pp. 355-607, 2019 | 1492 | 2019 |
| Iterative Bregman projections for regularized transportation problems JD Benamou, G Carlier, M Cuturi, L Nenna, G Peyré SIAM Journal on Scientific Computing 37 (2), A1111-A1138, 2015 | 623 | 2015 |
| Fast computation of Wasserstein barycenters M Cuturi, A Doucet Proceedings of the International Conference on Machine Learning 2014, JMLR …, 2014 | 569 | 2014 |
| Convolutional Wasserstein Distances: Efficient Optimal Transportation on Geometric Domains J Solomon, F de Goes, G Peyré, M Cuturi, A Butscher, A Nguyen, T Du, ... ACM Transactions on Graphics (TOG) SIGGRAPH, 2015, 2015 | 525 | 2015 |
| Learning Generative Models with Sinkhorn Divergences A Genevay, G Peyré, M Cuturi Proceedings of the Twenty-First International Conference on Artifical …, 2017 | 461* | 2017 |
| Fast global alignment kernels M Cuturi International Conference in Machine Learning 2011, 2011 | 353 | 2011 |
| Stochastic optimization for large-scale optimal transport A Genevay, M Cuturi, G Peyré, F Bach arXiv preprint arXiv:1605.08527, 2016 | 349 | 2016 |
| Soft-DTW: a differentiable loss function for time-series M Cuturi, M Blondel Proceedings of the 34th International Conference on Machine Learning, PMLR …, 2017 | 341 | 2017 |
| A kernel for time series based on global alignments M Cuturi, JP Vert, O Birkenes, T Matsui Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE …, 2007 | 269 | 2007 |
| On wasserstein two-sample testing and related families of nonparametric tests A Ramdas, NG Trillos, M Cuturi Entropy 19 (2), 47, 2017 | 258 | 2017 |
| Gromov-Wasserstein averaging of kernel and distance matrices G Peyré, M Cuturi, J Solomon International Conference on Machine Learning, 2664-2672, 2016 | 205 | 2016 |
| Sliced Wasserstein kernel for persistence diagrams M Carriere, M Cuturi, S Oudot International conference on machine learning, 664-673, 2017 | 190 | 2017 |
| A smoothed dual approach for variational wasserstein problems M Cuturi, G Peyré SIAM J. Imaging Sciences 9 (1), 320–343, 2016 | 169 | 2016 |
| Sample complexity of sinkhorn divergences A Genevay, L Chizat, F Bach, M Cuturi, G Peyré The 22nd international conference on artificial intelligence and statistics …, 2019 | 166 | 2019 |
| Fast dictionary learning with a smoothed Wasserstein loss A Rolet, M Cuturi, G Peyré Artificial Intelligence and Statistics, 630-638, 2016 | 130 | 2016 |
| Wasserstein Barycentric Coordinates: Histogram Regression Using Optimal Transport N Bonneel, G Peyré, M Cuturi ACM Transactions on Graphics 35 (4), 2016 | 127 | 2016 |
| Semigroup kernels on measures. M Cuturi, K Fukumizu, JP Vert Journal of Machine Learning Research 6 (7), 2005 | 109 | 2005 |
| Wasserstein training of restricted Boltzmann machines G Montavon, KR Müller, M Cuturi Advances in Neural Information Processing Systems 29, 2016 | 105* | 2016 |
| Wasserstein dictionary learning: Optimal transport-based unsupervised nonlinear dictionary learning MA Schmitz, M Heitz, N Bonneel, F Ngole, D Coeurjolly, M Cuturi, G Peyré, ... SIAM Journal on Imaging Sciences 11 (1), 643-678, 2018 | 103 | 2018 |
Gabriel PeyréCNRS, DMA, Ecole Normale Supérieure確認したメール アドレス: ens.fr
