{"id":64,"date":"2020-10-09T16:29:43","date_gmt":"2020-10-09T14:29:43","guid":{"rendered":"https:\/\/project.inria.fr\/dynalearn\/?page_id=64"},"modified":"2024-02-26T16:24:16","modified_gmt":"2024-02-26T15:24:16","slug":"presentation","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/dynalearn\/presentation\/","title":{"rendered":"Publications"},"content":{"rendered":"

Optimal Transport and flows in probability space<\/h4>\n
Subspace Detours Meet Gromov-Wasserstein<\/strong><\/div>\n
Bonet Cl\u00e9ment, Vayer Titouan, Courty Nicolas, Septier Fran\u00e7ois, Drumetz Lucas<\/div>\n
Algorithms<\/i>, MDPI, 2021, Special Issue Optimal Transport: Algorithms and Applications, 14, pp.1-29.<\/div>\n

https:\/\/arxiv.org\/abs\/2110.10932<\/a><\/p>\n

Sliced-Wasserstein Gradient Flows<\/strong><\/p>\n

Bonet, C., Courty, N., Septier, F., & Drumetz, L. (2022). Efficient gradient flows in sliced-Wasserstein space. Transactions on Machine Learning Research<\/i>.<\/div>\n
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Also presented (Spotlight) at the NeurIPS Workshop on Optimal Transport and Machine Learning (OTML)<\/p>\n

https:\/\/otml2021.github.io\/papers<\/a><\/p>\n

Sliced Optimal Transport and Riemannian Geometry<\/strong><\/p>\n

Bonet, C., Berg, P., Courty, N., Septier, F., Drumetz, L., & Pham, M. T. (2022, September). Spherical Sliced-Wasserstein. In The Eleventh International Conference on Learning Representations<\/i>.<\/div>\n
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https:\/\/arxiv.org\/pdf\/2206.08780.pdf<\/a><\/div>\n
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Bonet, C., Chapel, L., Drumetz, L., & Courty, N. (2023, September). Hyperbolic sliced-wasserstein via geodesic and horospherical projections. In Topological, Algebraic and Geometric Learning Workshops 2023<\/i> (pp. 334-370). PMLR.<\/div>\n<\/div>\n
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https:\/\/proceedings.mlr.press\/v221\/bonet23a\/bonet23a.pdf<\/a><\/div>\n
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Bonet, C., Mal\u00e9zieux, B., Rakotomamonjy, A., Drumetz, L., Moreau, T., Kowalski, M., & Courty, N. (2023, July). Sliced-Wasserstein on symmetric positive definite matrices for M\/EEG signals. In International Conference on Machine Learning<\/i> (pp. 2777-2805). PMLR.<\/div>\n<\/div>\n<\/div>\n
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https:\/\/proceedings.mlr.press\/v202\/bonet23a\/bonet23a.pdf<\/a><\/div>\n
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Sliced and Unbalanced Optimal Transport<\/strong><\/div>\n
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S\u00e9journ\u00e9, T., Bonet, C., Fatras, K., Nadjahi, K., & Courty, N. (2023). Unbalanced Optimal Transport meets Sliced-Wasserstein. arXiv preprint arXiv:2306.07176<\/i>.<\/div>\n<\/div>\n
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https:\/\/arxiv.org\/pdf\/2306.07176.pdf<\/a><\/div>\n
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Fast Optimal Transport through Sliced Wasserstein Generalized Geodesics<\/strong><\/div>\n
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Mahey, G., Chapel, L., Gasso, G., Bonet, C., & Courty, N. (2024). Fast Optimal Transport through Sliced Generalized Wasserstein Geodesics. Advances in Neural Information Processing Systems<\/i>, 36<\/i>.<\/div>\n<\/div>\n
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https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/file\/6f1346bac8b02f76a631400e2799b24b-Paper-Conference.pdf<\/a><\/div>\n
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Super-resolution in Physical Imaging<\/h4>\n

Post Processing Sparse And Instantaneous 2D Velocity Fields Using Physics-Informed Neural Networks<\/strong><\/p>\n

Di Carlo, Diego and Heitz, Dominique and Corpetti, Thomas,<\/p>\n

20th international symposium on applications of laser techniques to fluid mechanic, Lisbon, 2022<\/p>\n

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Dynamical formulation of the learning process<\/h4>\n
Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows<\/strong><\/div>\n
Guillaume Morel, Lucas Drumetz, Simon Bena\u00efchouche, Nicolas Courty, Fran\u00e7ois Rousseau<\/div>\n
Transactions on Machine Learning Research<\/i>, 2023.<\/div>\n

https:\/\/arxiv.org\/abs\/2209.10873<\/a><\/p>\n

Also presented in 2022 at NeurIPS workshop “The symbiosis of deep learning and differential equations II”: https:\/\/nips.cc\/virtual\/2022\/59912<\/a><\/p>\n

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Optimal Transport and flows in probability space Subspace Detours Meet Gromov-Wasserstein Bonet Cl\u00e9ment, Vayer Titouan, Courty Nicolas, Septier Fran\u00e7ois, Drumetz Lucas Algorithms, MDPI, 2021, Special\u2026<\/p>\n

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