20 citations · 25 across the 5 of their papers we have counts for
3 papers · 1 filter
A prior-based approximate latent Riemannian metric
Georgios Arvanitidis, Bogdan Georgiev, Bernhard Schölkopf
Stochastic generative models enable us to capture the geometric structure of a data manifold lying in a high dimensional space through a Riemannian metric in the latent space. Howe…
Geometrically Enriched Latent Spaces
Georgios Arvanitidis, Søren Hauberg, Bernhard Schölkopf
A common assumption in generative models is that the generator immerses the latent space into a Euclidean ambient space. Instead, we consider the ambient space to be a Riemannian m…
Fast and Robust Shortest Paths on Manifolds Learned from Data
Georgios Arvanitidis, Søren Hauberg, Philipp Hennig +1
We propose a fast, simple and robust algorithm for computing shortest paths and distances on Riemannian manifolds learned from data. This amounts to solving a system of ordinary di…