Challenges and Prospects of ML for the Physical Sciences

Contribution List

32. Joan Bruna

Joan Bruna (NYU / CCM)

6/13/22, 9:00 AM

Exponential Separations in Symmetric and Anti-Symmetric Neural Networks

3. Alberto Bietti

Alberto Bietti (NYU)

6/13/22, 9:45 AM

Benefits of convolutional models: a kernel perspective

5. Yi Ma

Yi Ma (University of California, Berkeley)

6/13/22, 11:00 AM

CTRL: Closed-Loop Data Transcription via Rate Reduction

6. Soledad Villar

Soledad Villar (Johns Hopkins University)

6/13/22, 11:45 AM

Dimensionless machine learning: Imposing exact units equivariance

35. CCN - Eero Simoncelli

6/13/22, 2:00 PM

Image Priors in the Era of Machine Learning

7. Jiequn Han

Jiequn Han (CCM, Flatiron Institute)

6/13/22, 2:30 PM

Developing reduced-order PDEs with machine learning-based closure models

10. Lawrence Saul

Lawrence Saul (UCSD/CCM )

6/14/22, 9:00 AM

The method of sparse similarity matching for high dimensional data analysis

28. SueYeon Chung

SueYeon Chung (CCN)

6/14/22, 9:45 AM

Efficient representation geometry in distributed neural networks

12. Rose Yu

Rose Yu (UCSD)

6/14/22, 11:00 AM

Approximately Equivariant Networks for Imperfectly Symmetric Dynamics

11. Léon Bottou

Léon Bottou (Facebook)

6/14/22, 11:45 AM

Out-of-distribution generalization and causation

14. Michael Mahoney

Michael Mahoney (UC Berkeley )

6/14/22, 2:30 PM

Continuous Network Models for Sequential Predictions

31. Kyle Cranmer

Kyle Cranmer (NYU)

6/14/22, 3:15 PM

17. Maarten de Hoop

Maarten de Hoop (Rice University )

6/15/22, 9:00 AM

Deep learning, active tectonics and planetary exploration

18. Marylou Gabrié

Marylou Gabrié (Ecole Polytechnique)

6/15/22, 9:45 AM

Enhancing Sampling with Learning

24. Shirley Ho

Shirley Ho (CCA )

6/15/22, 11:00 AM

Interpretable Machine Learning for Astrophysics

20. Giuseppe Carleo

Giuseppe Carleo (École Polytechnique)

6/15/22, 11:45 AM

Variational quantum states in the age of machine learning

21. Wenda Zhou

Wenda Zhou (NYU/CCM )

6/15/22, 2:30 PM

Spatial equivariance and deep networks: practical approaches and challenges

33. Tommaso Biancalani

Tommaso Biancalani (Genentech )

6/15/22, 3:15 PM

Deep learning for virtual screens of antibiotics

13. Robert Gower

Robert Gower (CCM )

6/16/22, 9:00 AM

Adaptive Stochastic Gradient Methods that leverage Interpolation

25. Eric Vanden Eijnden

Eric Vanden Eijnden (NYU)

6/16/22, 9:45 AM

Probability flow solution of the Fokker-Planck equation

34. Leslie Greengard

Leslie Greengard (CCM )

6/16/22, 11:00 AM

Electromagnetic/acoustic design optimization

19. Yongji Wang

Yongji Wang (Princeton University )

6/16/22, 11:45 AM

Physics-informed Neural Networks for fluid dynamics

26. Miles Cranmer

Miles Cranmer (Princeton University/CCA)

6/16/22, 2:30 PM

One Trick to Improve Regularized Training of Neural Networks

27. Stéphane Mallat

Stéphane Mallat (Collège de France/CCM )

6/16/22, 3:15 PM

Learning and Generating Multiscale Physics

Back to the event