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Renormalized Mutual Information for Artificial Scientific Discovery

MPG-Autoren
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Sarra,  Leopoldo
International Max Planck Research School, Max Planck Institute for the Science of Light, Max Planck Society;
Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society;
Department of Physics, Friedrich-Alexander Universität Erlangen-Nürnberg;

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Aiello,  Andrea
Genes Research Group, Research Groups, Max Planck Institute for the Science of Light, Max Planck Society;

/persons/resource/persons201125

Marquardt,  Florian
Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society;
Department of Physics, Friedrich-Alexander Universität Erlangen-Nürnberg;

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PhysRevLett.126.200601.pdf
(beliebiger Volltext), 795KB

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2020_Sarra.png
(Ergänzendes Material), 74KB

Zitation

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.


Zitierlink: https://hdl.handle.net/21.11116/0000-0006-89F5-B
Zusammenfassung
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.