5 citations · 9 across the 2 of their papers we have counts for
4 papers
flowMC: Normalizing-flow enhanced sampling package for probabilistic inference in Jax
Kaze W. K. Wong, Marylou Gabrié, Daniel Foreman-Mackey
flowMC is a Python library for accelerated Markov Chain Monte Carlo (MCMC) leveraging deep generative modeling. It is built on top of the machine learning libraries JAX and Flax. A…
Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals
James A. Brofos, Marylou Gabrié, Marcus A. Brubaker +1
Markov Chain Monte Carlo (MCMC) methods are a powerful tool for computation with complex probability distributions. However the performance of such methods is critically dependant…
Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods
Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden
Normalizing flows can generate complex target distributions and thus show promise in many applications in Bayesian statistics as an alternative or complement to MCMC for sampling p…
On the interplay between data structure and loss function in classification problems
Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun +1
One of the central puzzles in modern machine learning is the ability of heavily overparametrized models to generalize well. Although the low-dimensional structure of typical datase…