Daniel Behrend-Uriarte

Learning Distances from Data with Normalizing Flows and Score Matching

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 that increases in regions of low probability density, shortest paths naturally follow the data manifold. Fermat distanc…