18-24 June 2017
Palacio de Congresos
Europe/Madrid timezone
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Contribution Parallel

Andalucía I
Theoretical Developments

RG inspired Machine Learning for lattice field theory


  • Prof. Yannick MEURICE

Primary authors


Machine learning has been a fast growing field of research in several areas dealing with large datasets. We report recent progress on using RG ideas in the context of machine learning. We discuss the correspondence between principal components analysis (PCA) and RG flows across the transition for worm configurations of the 2D Ising model. More generally, we discuss the relationship between PCA and observables in MC data and the possibility of reduction of the number of learning parameters in supervised learning based on RG inspired hierarchical ansatzes.

Preferred track (if multiple tracks have been selected)

Theoretical Developments