Conference proceeding
RG-inspired machine learning for lattice field theory
EPJ Web of Conferences, Vol.175, p.11025
01/01/2018
DOI: 10.1051/epjconf/201817511025
Abstract
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.
Details
- Title: Subtitle
- RG-inspired machine learning for lattice field theory
- Creators
- Sam ForemanJoel Giedt - Rensselaer Polytechnic InstituteYannick Meurice - University of IowaJudah Unmuth-Yockey - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- EPJ Web of Conferences, Vol.175, p.11025
- DOI
- 10.1051/epjconf/201817511025
- ISSN
- 2101-6275
- eISSN
- 2100-014X
- Publisher
- EDP Sciences
- Language
- English
- Date published
- 01/01/2018
- Academic Unit
- Physics and Astronomy
- Record Identifier
- 9984429047402771
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