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Interpreting machine learning functions as physical observables
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Dimitrios Bachtis,
Biagio Lucini
Proceedings of The 38th International Symposium on Lattice Field Theory — PoS(LATTICE2021), Volume: 396
Swansea University Authors:
Gert Aarts , Dimitrios Bachtis, Biagio Lucini
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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...
| 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/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. |
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| College: |
College of Science |
| Funders: |
ERC, STFC. Leverhulme Foundation, Royal Society, ERDF |