no code implementations • 6 Jun 2024 • Bastian Boll, Daniel Gonzalez-Alvarado, Stefania Petra, Christoph Schnörr
The approach uses measure transport by randomized assignment flows on the statistical submanifold of factorizing distributions, which also enables to sample efficiently from the target distribution and to assess the likelihood of unseen data points.
no code implementations • 12 Feb 2024 • 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.
no code implementations • 30 Jun 2023 • Jonathan Schwarz, Jonas Cassel, Bastian Boll, Martin Gärttner, Peter Albers, Christoph Schnörr
This paper introduces assignment flows for density matrices as state spaces for representing and analyzing data associated with vertices of an underlying weighted graph.
no code implementations • 9 May 2022 • Dmitrij Sitenko, Bastian Boll, Christoph Schnörr
We devise an entropy-regularized difference-of-convex-functions (DC) decomposition of this potential and show that the basic geometric Euler scheme for integrating the assignment flow is equivalent to solving the G-PDE by an established DC programming scheme.
1 code implementation • 26 Jan 2022 • Bastian Boll, Alexander Zeilmann, Stefania Petra, Christoph Schnörr
We propose a novel class of deep stochastic predictors for classifying metric data on graphs within the PAC-Bayes risk certification paradigm.