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Interpreting machine learning functions as physical observables

Gert Aarts Orcid Logo, Dimitrios Bachtis, Biagio Lucini Orcid Logo

Proceedings of The 38th International Symposium on Lattice Field Theory — PoS(LATTICE2021), Volume: 396

Swansea University Authors: Gert Aarts Orcid Logo, Dimitrios Bachtis, Biagio Lucini Orcid Logo

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DOI (Published version): 10.22323/1.396.0248

Abstract

We propose to interpret machine learning functions as physical observables, opening up the possibility to apply “standard” statistical-mechanical methods to outputs from neural networks. This includes histogram reweighting and finite-size scaling, to analyse phase transitions quantitatively. In addi...

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Published in: Proceedings of The 38th International Symposium on Lattice Field Theory — PoS(LATTICE2021)
ISSN: 1824-8039
Published: Trieste, Italy Sissa Medialab 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa60430
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Abstract: We propose to interpret machine learning functions as physical observables, opening up the possibility to apply “standard” statistical-mechanical methods to outputs from neural networks. This includes histogram reweighting and finite-size scaling, to analyse phase transitions quantitatively. In addition we incorporate predictive functions as conjugate variables coupled to an external field within the Hamiltonian of a system, allowing to induce order-disorder phase transitions in a novel manner. A noteworthy feature of this approach is that no knowledge of the symmetries in the Hamiltonian is required.
College: College of Science
Funders: ERC, STFC. Leverhulme Foundation, Royal Society, ERDF