4 papers
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…
Training Deep Neural Networks by optimizing over nonlocal paths in hyperparameter space
Vlad Pushkarov, Jonathan Efroni, Mykola Maksymenko +1
Hyperparameter optimization is both a practical issue and an interesting theoretical problem in training of deep architectures. Despite many recent advances the most commonly used…
An exactly soluble model for a fractionalized Weyl semimetal
Fabian Hotz, Apoorv Tiwari, Oguz Turker +4
We construct an exactly solvable lattice model of a fractional Weyl semimetal (FWS). The low energy theory of this strongly interacting state is that of a Weyl semimetal built out…
Optimal Renormalization Group Transformation from Information Theory
Patrick M. Lenggenhager, Doruk Efe Gökmen, Zohar Ringel +2
Recently a novel real-space RG algorithm was introduced, identifying the relevant degrees of freedom of a system by maximizing an information-theoretic quantity, the real-space mut…