6 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…
Riemannian Patch Assignment Gradient Flows
Daniel Gonzalez-Alvarado, Fabio Schlindwein, Jonas Cassel +3
This paper introduces patch assignment flows for metric data labeling on graphs. Labelings are determined by regularizing initial local labelings through the dynamic interaction of…
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…
Expectation and Variance of the Degree of a Node in Random Spanning Trees
Enrique Fita SanmartÃn, Christoph Schnörr, Fred A. Hamprecht
We consider a Gibbs distribution over all spanning trees of an undirected, edge weighted finite graph, where, up to normalization, the probability of each tree is given by the prod…
The Central Spanning Tree Problem
Enrique Fita SanmartÃn, 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…