no code implementations • 5 Oct 2023 • Michael S. Albergo, Nicholas M. Boffi, Michael Lindsey, Eric Vanden-Eijnden
Given a set of $K$ probability densities, we consider the multimarginal generative modeling problem of learning a joint distribution that recovers these densities as marginals.
no code implementations • 3 May 2023 • Yuehaw Khoo, Michael Lindsey, Hongli Zhao
Fueled by the expressive power of deep neural networks, normalizing flows have achieved spectacular success in generative modeling, or learning to draw new samples from a distribution given a finite dataset of training samples.
no code implementations • 11 Nov 2022 • huan zhang, Robert J. Webber, Michael Lindsey, Timothy C. Berkelbach, Jonathan Weare
The use of neural network parametrizations to represent the ground state in variational Monte Carlo (VMC) calculations has generated intense interest in recent years.
no code implementations • 19 Jun 2021 • Robert J. Webber, Michael Lindsey
Second, in order to realize this favorable comparison in the presence of stochastic noise, we analyze the effect of sampling error on VMC parameter updates and experimentally demonstrate that it can be reduced by the parallel tempering method.