56 citations · 199 across the 40 of their papers we have counts for
46 papers
Generative Modeling of Discrete Data Using Geometric Latent Subspaces
Daniel Gonzalez-Alvarado, Jonas Cassel, Stefania Petra +1
We propose a geometric latent-subspace framework for generative modeling of discrete data. Specifically, we introduce latent subspaces in the exponential parameter space of product…
Sigma Flows for Image and Data Labeling and Learning Structured Prediction
Jonas Cassel, Bastian Boll, Stefania Petra +2
This paper introduces the sigma flow model for the prediction of structured labelings of data observed on Riemannian manifolds, including Euclidean image domains as special case. T…
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…
Generative Assignment Flows for Representing and Learning Joint Distributions of Discrete Data
Bastian Boll, Daniel Gonzalez-Alvarado, Stefania Petra +1
We introduce a novel generative model for the representation of joint probability distributions of a possibly large number of discrete random variables. The approach uses measure t…
The Central Spanning Tree Problem
Enrique Fita Sanmartín, Christoph Schnörr, Fred A. Hamprecht
Spanning trees are an important primitive in many data analysis tasks, when a data set needs to be summarized in terms of its "skeleton", or when a tree-shaped graph over all obser…
Generative Modeling of Discrete Joint Distributions by E-Geodesic Flow Matching on Assignment Manifolds
Bastian Boll, Daniel Gonzalez-Alvarado, Christoph Schnörr
This paper introduces a novel generative model for discrete distributions based on continuous normalizing flows on the submanifold of factorizing discrete measures. Integration of…