26 citations · 156 across the 36 of their papers we have counts for
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Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel
Paul Hagemann, Johannes Hertrich, Fabian Altekrüger +3
We propose conditional flows of the maximum mean discrepancy (MMD) with the negative distance kernel for posterior sampling and conditional generative modeling. This MMD, which is…
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…
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-…
Prony-Based Super-Resolution Phase Retrieval of Sparse, Multivariate Signals
Robert Beinert, Saghar Rezaei
Phase retrieval consists in the recovery of an unknown signal from phaseless measurements of its usually complex-valued Fourier transform. Without further assumptions, this problem…
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…