26 citations · 91 across the 18 of their papers we have counts for
12 papers · 1 filter
On a linear Gromov-Wasserstein distance
Florian Beier, Robert Beinert, Gabriele Steidl
Gromov-Wasserstein distances are generalization of Wasserstein distances, which are invariant under distance preserving transformations. Although a simplified version of optimal tr…
Generalized Normalizing Flows via Markov Chains
Paul Hagemann, Johannes Hertrich, Gabriele Steidl
Normalizing flows, diffusion normalizing flows and variational autoencoders are powerful generative models. This chapter provides a unified framework to handle these approaches via…
Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint
Paul Hagemann, Johannes Hertrich, Gabriele Steidl
To overcome topological constraints and improve the expressiveness of normalizing flow architectures, Wu, Köhler and Noé introduced stochastic normalizing flows which combine deter…
On the Dynamical System of Principal Curves in
Robert Beinert, Arian Bërdëllima, Manuel Gräf +1
Principal curves are natural generalizations of principal lines arising as first principal components in the Principal Component Analysis. They can be characterized from a stochast…
An Image Registration Model in Electron Backscatter Diffraction
Manuel Gräf, Sebastian Neumayer, Ralf Hielscher +3
Recently, variational methods were successfully applied for computing the optical flow in gray and RGB-valued image sequences. A crucial assumption in these models is that pixel-va…
Sparse Mixture Models inspired by ANOVA Decompositions
Johannes Hertrich, Fatima Antarou Ba, Gabriele Steidl
Inspired by the analysis of variance (ANOVA) decomposition of functions we propose a Gaussian-Uniform mixture model on the high-dimensional torus which relies on the assumption tha…