Normalizing flows for lattice gauge theory in arbitrary space-time dimension

no code implementations • 3 May 2023 • Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

Applications of normalizing flows to the sampling of field configurations in lattice gauge theory have so far been explored almost exclusively in two space-time dimensions.

Aspects of scaling and scalability for flow-based sampling of lattice QCD

no code implementations • 14 Nov 2022 • Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing.

Score-Based Diffusion meets Annealed Importance Sampling

1 code implementation • 16 Aug 2022 • Arnaud Doucet, Will Grathwohl, Alexander G. D. G. Matthews, Heiko Strathmann

To obtain an importance sampling estimate of the marginal likelihood, AIS introduces an extended target distribution to reweight the Markov chain proposal.

Continual Repeated Annealed Flow Transport Monte Carlo

2 code implementations • 31 Jan 2022 • Alexander G. D. G. Matthews, Michael Arbel, Danilo J. Rezende, Arnaud Doucet

We propose Continual Repeated Annealed Flow Transport Monte Carlo (CRAFT), a method that combines a sequential Monte Carlo (SMC) sampler (itself a generalization of Annealed Importance Sampling) with variational inference using normalizing flows.

Variational Inference

Annealed Flow Transport Monte Carlo

3 code implementations • 15 Feb 2021 • Michael Arbel, Alexander G. D. G. Matthews, Arnaud Doucet

Annealed Importance Sampling (AIS) and its Sequential Monte Carlo (SMC) extensions are state-of-the-art methods for estimating normalizing constants of probability distributions.