![Wasserstein gradient flows and applications to sampling in machine learning - lecture 1](/media/cache/video_light/uploads/video/2024-06-27_Korba_1.mp4-ac41ae47d3aaefd583e7caeb0e1f67de-video-d949aa4f1b7f39fc00857bfe03c5db8e.jpg)
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Wasserstein gradient flows and applications to sampling in machine learning - lecture 1
By Anna Korba
![Wasserstein gradient flows and applications to sampling in machine learning - lecture 2](/media/cache/video_light/uploads/video/2024-06-28_Korba_2.mp4-8fb84cfcaf072029bb6e130ff5627477-video-df0da8ea91844e1127e99379e8717d92.jpg)
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Wasserstein gradient flows and applications to sampling in machine learning - lecture 2
By Anna Korba
![Wasserstein gradient flows and applications to sampling in machine learning - lecture 3](/media/cache/video_light/uploads/video/2024-06-28_Korba_3.mp4-2582b6a7a5e3bbe9e8fd4c0e5baf4edc-video-a34b24a010447ee04012cbfb587326cc.jpg)
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Wasserstein gradient flows and applications to sampling in machine learning - lecture 3
By Anna Korba
![Project orange: Parabolic maximal regularity and the Kato square root property](/media/cache/video_light/uploads/video/2024-06-17_projet_orange-6f0cc16867030d6fb61a3dc2e4a8d3ec.jpg)
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Project orange: Parabolic maximal regularity and the Kato square root property
By Wolfgang Arendt , Azam Jahandideh , Vinzenzo Leone , Henning Heister , Manuel Schlierf , Sofian Abahmami
![An implicit formulation for incorporating different priors into a deformation model](/media/cache/video_light/uploads/video/2024-05-28_Gris.mp4-f6c4ec5c58d085b334b6ec78f08566a6-video-527c034210f79de45cc8656956e599b4.jpg)
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An implicit formulation for incorporating different priors into a deformation model
By Barbara Gris