20 citations · 25 across the 3 of their papers we have counts for
5 papers
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…
Variational Autoencoders with Riemannian Brownian Motion Priors
Dimitris Kalatzis, David Eklund, Georgios Arvanitidis +1
Variational Autoencoders (VAEs) represent the given data in a low-dimensional latent space, which is generally assumed to be Euclidean. This assumption naturally leads to the commo…
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…
Geodesic Clustering in Deep Generative Models
Tao Yang, Georgios Arvanitidis, Dongmei Fu +2
Deep generative models are tremendously successful in learning low-dimensional latent representations that well-describe the data. These representations, however, tend to much dist…