95 citations · 768 across the 58 of their papers we have counts for
8 papers · 1 filter
A Study of Particle Motion in the Presence of Clusters
Christian Wald, Gabriele Steidl
The motivation for this study came from the task of analysing the kinetic behavior of single molecules in a living cell based on Single Molecule Localization Microscopy. Given meas…
Denoising of Sphere- and SO(3)-Valued Data by Relaxed Tikhonov Regularization
Robert Beinert, Jonas Bresch, Gabriele Steidl
Manifold-valued signal- and image processing has received attention due to modern image acquisition techniques. Recently, a convex relaxation of the otherwise nonconvex Tikhonov-re…
Generalized Iterative Scaling for Regularized Optimal Transport with Affine Constraints: Application Examples
Johannes von Lindheim, Gabriele Steidl
We demonstrate the relevance of an algorithm called generalized iterative scaling (GIS) or simultaneous multiplicative algebraic reconstruction technique (SMART) and its rescaled b…
Sliced Optimal Transport on the Sphere
Michael Quellmalz, Robert Beinert, Gabriele Steidl
Sliced optimal transport reduces optimal transport on multi-dimensional domains to transport on the line. More precisely, sliced optimal transport is the concatenation of the well-…
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems
Fabian Altekrüger, Paul Hagemann, Gabriele Steidl
Conditional generative models became a very powerful tool to sample from Bayesian inverse problem posteriors. It is well-known in classical Bayesian literature that posterior measu…
Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation
Paul Hagemann, Sophie Mildenberger, Lars Ruthotto +2
Score-based diffusion models (SBDM) have recently emerged as state-of-the-art approaches for image generation. Existing SBDMs are typically formulated in a finite-dimensional setti…
Wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line
Johannes Hertrich, Robert Beinert, Manuel Gräf +1
This paper provides results on Wasserstein gradient flows between measures on the real line. Utilizing the isometric embedding of the Wasserstein space in…
Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels
Fabian Altekrüger, Johannes Hertrich, Gabriele Steidl
Wasserstein gradient flows of maximum mean discrepancy (MMD) functionals with non-smooth Riesz kernels show a rich structure as singular measures can become absolutely continuous o…