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Extending machine learning classification capabilities with histogram reweighting

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

Physical Review E, Volume: 102, Issue: 3

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

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DOI (Published version): 10.1103/physreve.102.033303

Abstract

We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learning methods. In our approach, we treat the output from a convolutional neural network as an observable in a statistical system, enabling its extrapolation over continuous ranges in parameter space. We...

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Published in: Physical Review E
ISSN: 2470-0045 2470-0053
Published: American Physical Society (APS) 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa55146
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spelling 2020-10-30T15:31:35.7009748 v2 55146 2020-09-09 Extending machine learning classification capabilities with histogram reweighting 91a311a58d3f8badc779f0ffa6d0ca3d Dimitrios Bachtis Dimitrios Bachtis true false 1ba0dad382dfe18348ec32fc65f3f3de 0000-0002-6038-3782 Gert Aarts Gert Aarts true false 7e6fcfe060e07a351090e2a8aba363cf 0000-0001-8974-8266 Biagio Lucini Biagio Lucini true false 2020-09-09 SPH We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learning methods. In our approach, we treat the output from a convolutional neural network as an observable in a statistical system, enabling its extrapolation over continuous ranges in parameter space. We demonstrate our proposal using the phase transition in the two-dimensional Ising model. By interpreting the output of the neural network as an order parameter, we explore connections with known observables in the system and investigate its scaling behavior. A finite-size scaling analysis is conducted based on quantities derived from the neural network that yields accurate estimates for the critical exponents and the critical temperature. The method improves the prospects of acquiring precision measurements from machine learning in physical systems without an order parameter and those where direct sampling in regions of parameter space might not be possible. Journal Article Physical Review E 102 3 American Physical Society (APS) 2470-0045 2470-0053 9 9 2020 2020-09-09 10.1103/physreve.102.033303 COLLEGE NANME Physics COLLEGE CODE SPH Swansea University 2020-10-30T15:31:35.7009748 2020-09-09T17:26:57.8583742 Professional Services ISS - Uncategorised Dimitrios Bachtis 1 Gert Aarts 0000-0002-6038-3782 2 Biagio Lucini 0000-0001-8974-8266 3 55146__18133__0971da316d804d35b2a594f6835ef70b.pdf paper-histogram-final-version.pdf 2020-09-09T18:14:33.6601047 Output 2240575 application/pdf Accepted Manuscript true false eng
title Extending machine learning classification capabilities with histogram reweighting
spellingShingle Extending machine learning classification capabilities with histogram reweighting
Dimitrios Bachtis
Gert Aarts
Biagio Lucini
title_short Extending machine learning classification capabilities with histogram reweighting
title_full Extending machine learning classification capabilities with histogram reweighting
title_fullStr Extending machine learning classification capabilities with histogram reweighting
title_full_unstemmed Extending machine learning classification capabilities with histogram reweighting
title_sort Extending machine learning classification capabilities with histogram reweighting
author_id_str_mv 91a311a58d3f8badc779f0ffa6d0ca3d
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author_id_fullname_str_mv 91a311a58d3f8badc779f0ffa6d0ca3d_***_Dimitrios Bachtis
1ba0dad382dfe18348ec32fc65f3f3de_***_Gert Aarts
7e6fcfe060e07a351090e2a8aba363cf_***_Biagio Lucini
author Dimitrios Bachtis
Gert Aarts
Biagio Lucini
author2 Dimitrios Bachtis
Gert Aarts
Biagio Lucini
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container_title Physical Review E
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publisher American Physical Society (APS)
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description We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learning methods. In our approach, we treat the output from a convolutional neural network as an observable in a statistical system, enabling its extrapolation over continuous ranges in parameter space. We demonstrate our proposal using the phase transition in the two-dimensional Ising model. By interpreting the output of the neural network as an order parameter, we explore connections with known observables in the system and investigate its scaling behavior. A finite-size scaling analysis is conducted based on quantities derived from the neural network that yields accurate estimates for the critical exponents and the critical temperature. The method improves the prospects of acquiring precision measurements from machine learning in physical systems without an order parameter and those where direct sampling in regions of parameter space might not be possible.
published_date 2020-09-09T04:09:09Z
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