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Deep learning and optimal transport: learning from one another. (Apprentissage profond et transport optimal: apprendre l'un de l'autre).. University of Southern Brittany, Vannes, Morbihan, France, (2021)Diffusion models with location-scale noise., , , and . CoRR, (2023)Proximal Splitting Meets Variance Reduction., , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1-10. PMLR, (2019)Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees., , and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 1288-1296. PMLR, (2024)SE(3)-Stochastic Flow Matching for Protein Backbone Generation., , , , , , , , , and . CoRR, (2023)Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation., , , , , , , , , and 2 other author(s). CoRR, (2024)No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths., , , and . ICML, OpenReview.net, (2024)POT: Python Optimal Transport., , , , , , , , , and 12 other author(s). J. Mach. Learn. Res., (2021)Pushing the right boundaries matters! Wasserstein Adversarial Training for Label Noise., , , , , and . CoRR, (2019)Optimal Transport meets Noisy Label Robust Loss and MixUp Regularization for Domain Adaptation., , and . CoLLAs, volume 199 of Proceedings of Machine Learning Research, page 966-981. PMLR, (2022)