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RG-inspired machine learning for lattice field theory by Foreman, Sam; Giedt, Joel; Meurice, Yannick; Unmuth-Yockey, Judah; Della Morte, M.; Fritzsch, P.; Gámiz Sánchez, E.; Pena Ruano, C. is a Physics and Astronomy article available to read on EtoBox.

What is RG-inspired machine learning for lattice field theory about?

Machine learning has been a fast growing field of research in several areas dealing with large datasets. We report recent attempts to use renormalization group (RG) ideas in the context of machine learning. We examine coarse graining procedures for perceptron models designed to identify the digits of the MNIST data. We discuss the correspondence between principal components analysis (PCA) and RG flows across the transition for worm configurations of the 2D Ising model. Preliminary results regarding the logarithmic divergence of the leading PCA eigenvalue were presented at the conference. More generally, we discuss the relationship between PCA and observables in Monte Carlo simulations and the possibility of reducing the number of learning parameters in supervised learning based on RG inspired hierarchical ansatzes.

Who reads RG-inspired machine learning for lattice field theory?

Author

Foreman, Sam; Giedt, Joel; Meurice, Yannick; Unmuth-Yockey, Judah; Della Morte, M.; Fritzsch, P.; Gámiz Sánchez, E.; Pena Ruano, C.

Publisher

EDP Sciences; Les Ulis: EDP Sciences, 2009- (ISSN 2100-014X)

Published

2018

Language

EN

Field

Physics and Astronomy (Physical Sciences)