3 papers
cs.LG2025
Beyond Diagonal Covariance: Flexible Posterior VAEs via Free-Form Injective Flows
Peter Sorrenson, Lukas Lührs, Hans Olischläger +1
Variational Autoencoders (VAEs) are powerful generative models widely used for learning interpretable latent spaces, quantifying uncertainty, and compressing data for downstream ge…
cs.LG2025
Learning Distances from Data with Normalizing Flows and Score Matching
Peter Sorrenson, Daniel Behrend-Uriarte, Christoph Schnörr +1
Density-based distances (DBDs) provide a principled approach to metric learning by defining distances in terms of the underlying data distribution. By employing a Riemannian metric…
cs.LG2024
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…