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cs.LG2024
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★ 3 cited
On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows
Felix Draxler, Stefan Wahl, Christoph Schnörr +1
We present a novel theoretical framework for understanding the expressive power of normalizing flows. Despite their prevalence in scientific applications, a comprehensive understan…