An Enquiry on similarities between Renormalization Group and Auto-Encoders using Transfer Learning

Abstract

Physicists have had a keen interest in the areas of Artificial Intelligence (AI) and Machine Learning (ML) for some time now with a special emphasis on unraveling the mechanism at the core of the process of learning. In particular, exploring the underlying mathematical structure of a neural net (NN) is expected to not only help us in understanding the epistemological meaning of 'Learning' but also has the potential to unravel the secrets behind the working of the brain. Here, it is worthwhile to establish correspondences and draw parallels between methods developed in core areas of Physics and the techniques developed at the forefront of AI and ML. Although recent explorations indicating a mapping between the Renormalisation Group (RG) and Deep Learning (DL) have shown valuable insights, we intend to investigate the relationship between RG and Autoencoders (AE) in particular. We will use Transfer Learning (TL) to embed the procedure of coarse-graining in a NN and compare it with the underlying mechanism of encoding-decoding through a series of tests.