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Machine Learning the Operator Content of the Critical Self-Dual Ising-Higgs Gauge Model
Lior Oppenheim, Maciej Koch-Janusz, Snir Gazit +1
We study the critical properties of the Ising-Higgs gauge theory in along the self-dual line which have recently been a subject of debate. For the first time, using machin…
Grokking as a First Order Phase Transition in Two Layer Networks
Noa Rubin, Inbar Seroussi, Zohar Ringel
A key property of deep neural networks (DNNs) is their ability to learn new features during training. This intriguing aspect of deep learning stands out most clearly in recently re…
Speed Limits for Deep Learning
Inbar Seroussi, Alexander A. Alemi, Moritz Helias +1
State-of-the-art neural networks require extreme computational power to train. It is therefore natural to wonder whether they are optimally trained. Here we apply a recent advancem…
Spectral-Bias and Kernel-Task Alignment in Physically Informed Neural Networks
Inbar Seroussi, Asaf Miron, Zohar Ringel
Physically informed neural networks (PINNs) are a promising emerging method for solving differential equations. As in many other deep learning approaches, the choice of PINN design…
Compression theory for inhomogeneous systems
Doruk Efe Gökmen, Sounak Biswas, Sebastian D. Huber +3
The physics of complex systems stands to greatly benefit from the qualitative changes in data availability and advances in data-driven computational methods. Many of these systems…