02. Machine Learning for Scientific Imaging (Julien Mairal)
[Slides]
03. Physics-informed Deep Learning for Inverse Problems (Ching-Yao Lai)
[Slides]
04. Poster Fast-Forward Session
[Slides]
05. AI for Physical Dynamics: from Neural Operators to Foundation Models (Patrick Gallinari)
[Slides]
06. Spherical Flows for Sampling Categorial Distributions (Gabriele Steidl)
[Slides]
07. Mathematics of Diffusion Models and their Applications for Image Inverse Problems (Samuel Hurault)
[Slides]
08. Mathematics of Diffusion Models and their Applications for Image Inverse Problems (demo) (Samuel Hurault)
[Slides]
09. Model-based Neural Networks + Focus on PnP and Multilevel (Nelly Pustelnik)
[Slides]
10. Foundation Models for Life Sciences (Linus Bleistein)
[Slides]
11. Data-Driven High-Dimensional Inverse Problems in Astrophysics (Laurence Perreault-Levasseur)
[Slides]
12. Simulation-based Inference (Jakob Macke)
[Slides]
13. Simulation-based Inference (demo) (Jakob Macke)
[Slides]
14. Foundation Models on the Earth System (Fanny Lehmann)
[Slides]