7 papers
Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once
Moritz Piening, Richard Duong, Gabriele Steidl
Flow matching has recently emerged as a flexible and efficient framework for generative modelling by learning deterministic transport dynamics between probability measures. In this…
Spherical Flows for Sampling Categorical Data
Jannis Chemseddine, Gregor Kornhardt, Gabriele Steidl
We study the problem of learning generative models for discrete sequences in a continuous embedding space. Whereas prior approaches typically operate in Euclidean space or on the p…
Adapting Noise to Data: Generative Flows from 1D Processes
Jannis Chemseddine, Gregor Kornhardt, Richard Duong +1
The default Gaussian latent in flow-based generative models poses challenges when learning certain distributions such as heavy-tailed ones. We introduce a general framework for lea…
Telegrapher's Generative Model via Kac Flows
Richard Duong, Jannis Chemseddine, Peter K. Friz +1
We break the mold in flow-based generative modeling by proposing a new model based on the damped wave equation, also known as telegrapher's equation. Similar to the diffusion equat…
Smoothed Distance Kernels for MMDs and Applications in Wasserstein Gradient Flows
Nicolaj Rux, Michael Quellmalz, Gabriele Steidl
Negative distance kernels were used in the definition of maximum mean discrepancies (MMDs) in statistics and lead to favorable numerical results in various ap…
Wasserstein Gradient Flows of MMD Functionals with Distance Kernels under Sobolev Regularization
Richard Duong, Nicolaj Rux, Viktor Stein +1
We consider Wasserstein gradient flows of maximum mean discrepancy (MMD) functionals for positive and negative distance kernels and…