Description
The adoption of machine learning (ML], into theoretical physics comes on the heels of an explosion of industry progress that started in 2012. Since that time, computer scientists have demonstrated that learning algorithms  those designed to respond and adapt to new data  provide an exceptionally powerful platform for tackling many difficult tasks in image recognition, natural language comprehension, game play and more. This new breed of ML algorithm has now conquered benchmarks previously thought to be decades away due to their high mathematical complexity. In the last several years, researchers at Perimeter have begun to examine machine learning algorithms for application to a new set of problems, including condensed matter, quantum information, numerical relativity, quantum gravity and astrophysics.
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Machine Learning Renormalization Group (VIRTUAL)
YiZhuang You University of California, San Diego

Scaling Limits of Bayesian Inference with Deep Neural Networks
Boris Hanin Princeton University

Measure Transport Perspectives on Sampling, Generative Modeling, and Beyond
Michael Albergo New York University (NYU)

Neuralnetwork quantum states for ultracold Fermi gases
Jane Kim Ohio University

Scalar and Grassmann Neural Network Field Theory
Anindita Maiti Perimeter Institute for Theoretical Physics


Topological quantum phase transitions in exact twodimensional isometric tensor networks  VIRTUAL
YuJie Liu Technical University of Munich (TUM)

Deep Learning Convolutions Through the Lens of Tensor Networks
Felix Dangel Vector Institute for Artificial Intelligence

Quantum metrology in the finitesample regime  VIRTUAL
Johannes Meyer Freie Universität Berlin



The Quantization Model of Neural Scaling
Eric Michaud Massachusetts Institute of Technology (MIT)