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Machine Learning and Statistical Physics

  • The Science Center (Rm 4102) at The Graduate Center, CUNY 365 5th Avenue New York, NY, 10016 United States (map)

The recent surge of activity at the interface of statistical physics and machine learning has brought novel tools and ideas to both fields. Some examples include the information bottleneck appearing as a fundamental lens through which to view neural networks, the renormalization group as a conceptual basis for understanding deep learning, and the identification of phases of matter using methods of machine learning. This workshop brings together a number of researchers taking a statistical physics approach to machine learning with the intention of using insights from physics to understand learning systems.

For more info and to register, visit the event site here.
Download full schedule pdf here.

Tuesday, November 13th
9:00am - Coffee & Bagels
9:30am - A Universal Jeffreys Prior - Jordan Cotler
10:00am - Machine learning for many-body quantum physics - Guiseppe Carleo
10:30am - Break
11:00am - Layer-wise greedy optimization with an eye for RG - Zohar Ringel
11:30am - Neuroscience-based machine learning - Dmitri Chklovskii
12:00pm - Lunch
2:00pm - Density estimation using field theory - Justin Kinney
2:30pm - Discrete priors on simplified models optimize channel capacity from noisy experiments - Benjamin Machta
3:00pm - Break
3:30pm - Learning Quantum Emergence with AI - Eun-Ah Kim
4:00pm - Monte Carlo Study of Small Feedforward Neural Networks - Ariana Mann

Wednesday, November 14th
9:00am - Coffee & Bagels
9:30am - Manifold Tiling with an Unsupervised Neural Net - Anirvan Sengupta
10:00am - Reinforcement Learning to Prepare Quantum States Away from Equilibrium - Marin Bukov
10:30am - Break
11:00am - Quantum control landscapes and the limits of learning - Dries Sels
11:30am - Alex Alemi
12:00pm - Lunch
2:00pm - Entropy & mutual information in models of deep neural networks - Marylou Gabrié
2:30pm - Sloppy models, Differential geometry, and How Science Works - Jim Sethna
3:00pm - Break
3:30pm - Visualizing Probabilities: Intensive Principal Component Analysis - Katherine Quinn
4:00pm - Just do the best you can: statistical physics approaches to reinforcement learning Chris Wiggins
4:30pm - Break
5:00pm - Panel Discussion

Thursday, November 15th
9:00am - Coffee & Bagels
9:30am - Which ReLU Net Architectures Give Rise to Exploding and Vanishing Gradients? - Boris Hanin
10:00am - Neural networks as interacting particle systems - Grant Rotskoff
10:30am - Break
11:00am - SGD Implicitly Regularizes Generalization Error - Dan Roberts
11:30am - Expressiveness in Deep Learning via Tensor Networks and Quantum Entanglement - Nadav Cohen
12:00pm - Normalizing Flows and Canonical Transformations - Austen Lamacraft
12:30pm - Lunch
2:00pm - Discussion

Sponsored by Institute for Complex Adaptive Matter (ICAM) and the Initiative for the Theoretical Sciences.

Later Event: November 16
Seeing beyond V1