2020/04/30 by Dimitrios Bachtis, Gert Aarts, Biagio Lucini · 35 citations
Computer Science · Physics and Astronomy · #Anomaly Detection Techniques and Applications #Artificial intelligence #Computer science #Generative Adversarial Networks and Image Synthesis #Histogram #Image (mathematics) #Machine learning #Neural Networks and Applications #Pattern recognition (psychology) #cond-mat.dis-nn #cond-mat.stat-mech #cs.LG #hep-lat #physics.comp-ph
paper · pdf · doi:10.1103/physreve.102.033303
published in Physical review. E 102(3), 033303 (American Physical Society)
openalex publication_date 2020/09/09 · arxiv created 2020/11/22 · arxiv updated 2020/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.