239 citations · 642 across the 12 of their papers we have counts for
Showing stat.MLShow all
3 papers · 1 filter
stat.ML2023★ 7 cited
Equivariant flow matching
Leon Klein, Andreas Krämer, Frank Noé
Normalizing flows are a class of deep generative models that are especially interesting for modeling probability distributions in physics, where the exact likelihood of flows allow…
stat.ML2021★ 2 cited
Smooth Normalizing Flows
Jonas Köhler, Andreas Krämer, Frank Noé
Normalizing flows are a promising tool for modeling probability distributions in physical systems. While state-of-the-art flows accurately approximate distributions and energies, a…
stat.ML2020★ 1 cited
Training Invertible Linear Layers through Rank-One Perturbations
Andreas Krämer, Jonas Köhler, Frank Noé
Many types of neural network layers rely on matrix properties such as invertibility or orthogonality. Retaining such properties during optimization with gradient-based stochastic o…