69 citations · 73 across the 4 of their papers we have counts for
4 papers
Split-Flows: Measure Transport and Information Loss Across Molecular Resolutions
Sander Hummerich, Tristan Bereau, Ullrich Köthe
By reducing resolution, coarse-grained models greatly accelerate molecular simulations, unlocking access to long-timescale phenomena, though at the expense of microscopic informati…
Learning Distributions on Manifolds with Free-Form Flows
Peter Sorrenson, Felix Draxler, Armand Rousselot +2
We propose Manifold Free-Form Flows (M-FFF), a simple new generative model for data on manifolds. The existing approaches to learning a distribution on arbitrary manifolds are expe…
Lifting Architectural Constraints of Injective Flows
Peter Sorrenson, Felix Draxler, Armand Rousselot +3
Normalizing Flows explicitly maximize a full-dimensional likelihood on the training data. However, real data is typically only supported on a lower-dimensional manifold leading the…
Generative Networks for Precision Enthusiasts
Anja Butter, Theo Heimel, Sander Hummerich +4
Generative networks are opening new avenues in fast event generation for the LHC. We show how generative flow networks can reach percent-level precision for kinematic distributions…