1 citations · 1 across the 5 of their papers we have counts for
5 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…
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…
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…
Quantum State Assignment Flows
Jonathan Schwarz, Jonas Cassel, Bastian Boll +3
This paper introduces assignment flows for density matrices as state spaces for representing and analyzing data associated with vertices of an underlying weighted graph. Determinin…