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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, Start page: 033303

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
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 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.
College: Faculty of Science and Engineering
Funders: The authors received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under grant agreement No. 813942. The work of G.A. and B.L. has been supported in part by the UKRI Science and Technology Facilities Council Consolidated Grant No. ST/P00055X/1. The work of B.L. is further supported in part by the Royal Society Wolfson Research Merit Award WM170010. Numerical simulations have been performed on the Swansea SUNBIRD system. This system is part of the Supercomputing Wales project, which is partly funded by the European Regional Development Fund (ERDF) via the Welsh Government. We thank COST Action CA15213 THOR for support.
Issue: 3
Start Page: 033303

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