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Why Deep Learning Works: Heavy-Tailed Random Matrix Theory as an Example of Physics Informed Machine Learning
Presenter
- Michael Mahoney
October 14, 2019
IPAM
Learning molecular model from simulation and experimental data
Presenter
- Cecilia Clementi
October 14, 2019
IPAM
Solving Quantum Many-Body Problems with Deep Neural Networks
Presenter
- Han Pu
September 27, 2019
IPAM
Generative and variational modeling for quantum many-body physics
Presenter
- Giuseppe Carleo
September 27, 2019
IPAM
Simulating quantum dynamics with neural machine translation
Presenter
- Juan Carrasquilla
September 27, 2019
IPAM
Euclidean Neural Networks* for Emulating Ab Initio Calculations and Generating Atomic Geometries *also called Tensor Field Networks and 3D Steerable CNNs
Presenter
- Tess Smidt
September 27, 2019
IPAM
Likelihood free generative modeling for high energy physics
Presenter
- Benjamin Nachman
September 27, 2019
IPAM
Generalizing Hamiltonian Monte Carlo with Neural Networks
Presenter
- Jascha Sohl-Dickstein
September 26, 2019
IPAM
Machine-learning for materials and physics discovery through symbolic regression and kernel methods
Presenter
- Richard Hennig
September 26, 2019
IPAM
A Numerical Analysis Perspective on Deep Neural Networks
Presenter
- Lars Ruthotto
September 25, 2019
IPAM