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

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  • Shukla, Mohak
  • Thakur, Ajay D.

Abstract

Physicists have had a keen interest in the areas of Artificial Intelligence (AI) and Machine Learning (ML) for a while, with a special inclination towards unravelling the fundamental mechanism behind the process of learning. In particular, exploring the underlying mathematical structure of a neural net (NN) is expected to not only help us understand the epistemological meaning of ‘Learning’ but also has the potential to unravel the secrets behind the workings 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 Renormalization Group (RG) and Deep Learning (DL) have shown valuable insights, we intend to investigate the relationship between RG and Autoencoders (AE) in particular. We use Transfer Learning (TL) to embed the coarse-graining procedure in a NN and compare it with the underlying mechanism of encoding–decoding through a series of tests.

Suggested Citation

  • Shukla, Mohak & Thakur, Ajay D., 2022. "An Enquiry on similarities between Renormalization Group and Auto-Encoders using Transfer Learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    • Handle: RePEc:eee:phsmap:v:608:y:2022:i:p1:s0378437122008342
    DOI: 10.1016/j.physa.2022.128276
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    1. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
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