Conference Paper/Proceeding/Abstract 701 views 65 downloads
Machine learning with quantum field theories
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Biagio Lucini
Proceedings of The 38th International Symposium on Lattice Field Theory — PoS(LATTICE2021), Volume: 396
Swansea University Authors:
Dimitrios Bachtis, Gert Aarts , Biagio Lucini
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DOI (Published version): 10.22323/1.396.0201
Abstract
The precise equivalence between discretized Euclidean field theories and a certain class of probabilistic graphical models, namely the mathematical framework of Markov random fields, opens up the opportunity to investigate machine learning from the perspective of quantum field theory. In this contri...
| Published in: | Proceedings of The 38th International Symposium on Lattice Field Theory — PoS(LATTICE2021) |
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| ISSN: | 1824-8039 |
| Published: |
Trieste, Italy
Sissa Medialab
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa60431 |
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| Abstract: |
The precise equivalence between discretized Euclidean field theories and a certain class of probabilistic graphical models, namely the mathematical framework of Markov random fields, opens up the opportunity to investigate machine learning from the perspective of quantum field theory. In this contribution we will demonstrate, through the Hammersley-Clifford theorem, that the ϕ4 scalar field theory on a square lattice satisfies the local Markov property and can therefore be recast as a Markov random field. We will then derive from the ϕ4 theory machine learning algorithms and neural networks which can be viewed as generalizations of conventional neural network architectures. Finally, we will conclude by presenting applications based on the minimization of an asymmetric distance between the probability distribution of the ϕ4 machine learning algorithms and target probability distributions. |
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| College: |
College of Science |
| Funders: |
ERC, STFC. Leverhulme Foundation, Royal Society, ERDF |