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Relevance in the Renormalization Group and in Information Theory
Amit Gordon, Aditya Banerjee, Maciej Koch-Janusz +1
The analysis of complex physical systems hinges on the ability to extract the relevant degrees of freedom from among the many others. Though much hope is placed in machine learning…
Intrinsic sign problem in fermionic and bosonic chiral topological matter
Omri Golan, Adam Smith, Zohar Ringel
The infamous sign problem leads to an exponential complexity in Monte Carlo simulations of generic many-body quantum systems. Nevertheless, many phases of matter are known to admit…
Intrinsic sign problems in topological quantum field theories
Adam Smith, Omri Golan, Zohar Ringel
The sign problem is a widespread numerical hurdle preventing us from simulating the equilibrium behavior of various problems at the forefront of physics. Focusing on an important s…
Predicting the outputs of finite deep neural networks trained with noisy gradients
Gadi Naveh, Oded Ben-David, Haim Sompolinsky +1
A recent line of works studied wide deep neural networks (DNNs) by approximating them as Gaussian Processes (GPs). A DNN trained with gradient flow was shown to map to a GP governe…