15 citations · 19 across the 2 of their papers we have counts for
3 papers
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…
Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization
Grant M. Rotskoff, Andrew R. Mitchell, Eric Vanden-Eijnden
Deep neural networks, when optimized with sufficient data, provide accurate representations of high-dimensional functions; in contrast, function approximation techniques that have…
Global convergence of neuron birth-death dynamics
Grant Rotskoff, Samy Jelassi, Joan Bruna +1
Neural networks with a large number of parameters admit a mean-field description, which has recently served as a theoretical explanation for the favorable training properties of "o…