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  Renormalized Mutual Information for Artificial Scientific Discovery

Sarra, L., Aiello, A., & Marquardt, F. (2021). Renormalized Mutual Information for Artificial Scientific Discovery. Physical Review Letters, 126: 200601. doi:10.1103/PhysRevLett.126.200601.

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 Creators:
Sarra, Leopoldo1, Author
Aiello, Andrea1, Author              
Marquardt, Florian1, 2, Author              
Affiliations:
1Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society, Staudtstraße 2, 91058 Erlangen, DE, ou_2421700              
2Department of Physics, Friedrich-Alexander Universität Erlangen-Nürnberg, 91058 Erlangen, DE, ou_persistent22              

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 Abstract: We derive a well-defined renormalized version of mutual information that allows to estimate the dependence between continuous random variables in the important case when one is deterministically dependent on the other. This is the situation relevant for feature extraction, where the goal is to produce a low-dimensional effective description of a high-dimensional system. Our approach enables the discovery of collective variables in physical systems, thus adding to the toolbox of artificial scientific discovery, while also aiding the analysis of information flow in artificial neural networks.

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 Dates: 2021-05-17
 Publication Status: Published online
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 Identifiers: DOI: 10.1103/PhysRevLett.126.200601
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Title: Physical Review Letters
  Abbreviation : Phys. Rev. Lett.
Source Genre: Journal
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Publ. Info: Woodbury, N.Y. : American Physical Society
Pages: - Volume / Issue: 126 Sequence Number: 200601 Start / End Page: - Identifier: ISSN: 0031-9007
CoNE: https://pure.mpg.de/cone/journals/resource/954925433406_1