Journal article 628 views 261 downloads
Extending machine learning classification capabilities with histogram reweighting
Dimitrios Bachtis,
Gert Aarts 
,
Biagio Lucini
,
Biagio Lucini
Physical Review E, Volume: 102, Issue: 3
Swansea University Authors:
Dimitrios Bachtis, Gert Aarts , Biagio Lucini
-
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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...
| Published in: | Physical Review E |
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| ISSN: | 2470-0045 2470-0053 |
| Published: |
American Physical Society (APS)
2020
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa55146 |
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| 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. |
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| College: |
Professional Services |
| Issue: |
3 |