1 code implementation • 16 May 2022 • Stephen Whitelam, Viktor Selin, Ian Benlolo, Corneel Casert, Isaac Tamblyn
We examine the zero-temperature Metropolis Monte Carlo algorithm as a tool for training a neural network by minimizing a loss function.
1 code implementation • 17 Feb 2022 • Corneel Casert, Isaac Tamblyn, Stephen Whitelam
We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of the system, and can accurately predict its emergent behavior under conditions not observed during training.
no code implementations • 17 Nov 2020 • Corneel Casert, Tom Vieijra, Stephen Whitelam, Isaac Tamblyn
We use a neural network ansatz originally designed for the variational optimization of quantum systems to study dynamical large deviations in classical ones.