Speaker
Vincent Stimper
(Max Planck Institute for Intelligent Systems)
Description
Sampling from Boltzmann distributions through normalizing flows promises to be computationally much cheaper than molecular dynamics (MD) simulations. However, flows struggle to approximate complicated target distributions due to topological constraints and still heavily rely on MD samples to be trained on. Here, we present two lines of research addressing these issues, the former by introducing a more expressive base distribution for normalizing flows and the latter through a novel bootstrapping training procedure using only samples from the flow as well as the density of the target.
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Primary authors
Vincent Stimper
(Max Planck Institute for Intelligent Systems)
Laurence Illing Midgley
Gregor Simm
Prof.
Bernhard Schölkopf
(Max Planck Institue for Intelligent Systems)
Jose Miguel Hernandez-Lobato
(University of Cambridge)
