1 citations · 2 across the 9 of their papers we have counts for
9 papers
Alignment Matters Inside and Out in Equivariant Graph Flow Matching
Moritz Piening, Christian Wald
Graphs are invariant under node permutations, motivating permutation-equivariant architectures in generative models. In flow matching, however, symmetry also affects the source-tar…
Self-Aware Markov Models for Discrete Reasoning
Gregor Kornhardt, Jannis Chemseddine, Christian Wald +1
Standard masked discrete diffusion models face limitations in reasoning tasks due to their inability to correct their own mistakes on the masking path. Since they rely on a fixed n…
Sampling via Stochastic Interpolants by Langevin-based Velocity and Initialization Estimation in Flow ODEs
Chenguang Duan, Yuling Jiao, Gabriele Steidl +3
We propose a novel method for sampling from unnormalized Boltzmann densities based on a probability flow ordinary differential equation (ODE) derived from linear stochastic interpo…
A Jump-Diffusion Framework for Irregular Time Series Generation
O. Pfohl, J. Chemseddine, P. Hagemann +3
We propose a framework for generative modeling of continuous-time processes from irregularly and asynchronously recorded data. It is based on the matching of generators and accommo…
Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans
Christian Wald, Gabriele Steidl
Among generative neural models, flow matching techniques stand out for their simple applicability and good scaling properties. Here, velocity fields of curves connecting a simple l…
Neural Sampling from Boltzmann Densities: Fisher-Rao Curves in the Wasserstein Geometry
Jannis Chemseddine, Christian Wald, Richard Duong +1
We deal with the task of sampling from an unnormalized Boltzmann density by learning a Boltzmann curve given by energies starting in a simple density . First, we e…
Sparse additive function decompositions facing basis transforms
Fatima Antarou Ba, Oleh Melnyk, Christian Wald +1
High-dimensional real-world systems can often be well characterized by a small number of simultaneous low-complexity interactions. The analysis of variance (ANOVA) decomposition an…
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
Jannis Chemseddine, Paul Hagemann, Gabriele Steidl +1
In inverse problems, many conditional generative models approximate the posterior measure by minimizing a distance between the joint measure and its learned approximation. While th…
Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distances
Jannis Chemseddine, Paul Hagemann, Christian Wald
In inverse problems, many conditional generative models approximate the posterior measure by minimizing a distance between the joint measure and its learned approximation. While th…