7 citations · 13 across the 8 of their papers we have counts for
8 papers
HOT-POT: Optimal Transport for Sparse Stereo Matching
Antonin Clerc, Michael Quellmalz, Moritz Piening +3
Stereo vision between images faces a range of challenges, including occlusions, motion, and camera distortions, across applications in autonomous driving, robotics, and face analys…
Stochastic Zeroth-Order Method for Computing Generalized Rayleigh Quotients
Jonas Bresch, Oleh Melnyk, Martin Schoen +1
The maximization of the (generalized) Rayleigh quotient is a central problem in numerical linear algebra. Conventional algorithms for its computation typically rely on matrix-adjoi…
Unsupervised Ground Metric Learning
Janis Auffenberg, Jonas Bresch, Oleh Melnyk +1
Data classification without access to labeled samples remains a challenging problem. It usually depends on an appropriately chosen distance between features, a topic addressed in m…
Optimizing Federated Learning by Entropy-Based Client Selection
Andreas Lutz, Gabriele Steidl, Karsten Müller +1
Although deep learning has revolutionized domains such as natural language processing and computer vision, its dependence on centralized datasets raises serious privacy concerns. F…
Slicing of Radial Functions: a Dimension Walk in the Fourier Space
Nicolaj Rux, Michael Quellmalz, Gabriele Steidl
Computations in high-dimensional spaces can often be realized only approximately, using a certain number of projections onto lower dimensional subspaces or sampling from distributi…
Time-Harmonic Optical Flow with Applications in Elastography
Oleh Melnyk, Michael Quellmalz, Gabriele Steidl +3
In this paper, we propose mathematical models for reconstructing the optical flow in time-harmonic elastography. In this image acquisition technique, the object undergoes a special…
Parallelly Sliced Optimal Transport on Spheres and on the Rotation Group
Michael Quellmalz, Léo Buecher, Gabriele Steidl
Sliced optimal transport, which is basically a Radon transform followed by one-dimensional optimal transport, became popular in various applications due to its efficient computatio…
Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphs
Martin Hanik, Gabriele Steidl, Christoph von Tycowicz
We propose two graph neural network layers for graphs with features in a Riemannian manifold. First, based on a manifold-valued graph diffusion equation, we construct a diffusion l…