5 citations · 5 across the 1 of their papers we have counts for
6 papers
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott, Michael S. Albergo, Aleksandar Botev +10
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…
Introduction to Normalizing Flows for Lattice Field Theory
Michael S. Albergo, Denis Boyda, Daniel C. Hackett +5
This notebook tutorial demonstrates a method for sampling Boltzmann distributions of lattice field theories using a class of machine learning models known as normalizing flows. The…
Equivariant flow-based sampling for lattice gauge theory
Gurtej Kanwar, Michael S. Albergo, Denis Boyda +5
We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge-invariant by construction. We demonstrate the application of this fram…
Normalizing Flows on Tori and Spheres
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière +4
Normalizing flows are a powerful tool for building expressive distributions in high dimensions. So far, most of the literature has concentrated on learning flows on Euclidean space…
The learnability scaling of quantum states: restricted Boltzmann machines
Dan Sehayek, Anna Golubeva, Michael S. Albergo +3
Generative modeling with machine learning has provided a new perspective on the data-driven task of reconstructing quantum states from a set of qubit measurements. As increasingly…
Flow-based generative models for Markov chain Monte Carlo in lattice field theory
M. S. Albergo, G. Kanwar, P. E. Shanahan
A Markov chain update scheme using a machine-learned flow-based generative model is proposed for Monte Carlo sampling in lattice field theories. The generative model may be optimiz…