95 citations · 768 across the 58 of their papers we have counts for
8 papers · 1 filter
Continuous Wavelet Frames on the Sphere: The Group-Theoretic Approach Revisited
S. Dahlke, F. De Mari, E. De Vito +5
In \cite{AV99}, Antoine and Vandergheynst propose a group-theoretic approach to continuous wavelet frames on the sphere. The frame is constructed from a single so-called admissible…
Convolutional Proximal Neural Networks and Plug-and-Play Algorithms
Johannes Hertrich, Sebastian Neumayer, Gabriele Steidl
In this paper, we introduce convolutional proximal neural networks (cPNNs), which are by construction averaged operators. For filters of full length, we propose a stochastic gradie…
PCA Reduced Gaussian Mixture Models with Applications in Superresolution
Johannes Hertrich, Dang Phoung Lan Nguyen, Jean-Fancois Aujol +4
Despite the rapid development of computational hardware, the treatment of large and high dimensional data sets is still a challenging problem. This paper provides a twofold contrib…
Transfer Operators from Optimal Transport Plans for Coherent Set Detection
Péter Koltai, Johannes von Lindheim, Sebastian Neumayer +1
The topic of this study lies in the intersection of two fields. One is related with analyzing transport phenomena in complicated flows.For this purpose, we use so-called coherent s…
Robust PCA via Regularized REAPER with a Matrix-Free Proximal Algorithm
Robert Beinert, Gabriele Steidl
Principal component analysis (PCA) is known to be sensitive to outliers, so that various robust PCA variants were proposed in the literature. A recent model, called REAPER, aims to…
Inertial Stochastic PALM (iSPALM) and Applications in Machine Learning
Johannes Hertrich, Gabriele Steidl
Inertial algorithms for minimizing nonsmooth and nonconvex functions as the inertial proximal alternating linearized minimization algorithm (iPALM) have demonstrated their superior…
A New Constrained Optimization Model for Solving the Nonsymmetric Stochastic Inverse Eigenvalue Problem
Gabriele Steidl, Maximilian Winkler
The stochastic inverse eigenvalue problem aims to reconstruct a stochastic matrix from its spectrum. While there exists a large literature on the existence of solutions for special…
From Optimal Transport to Discrepancy
Sebastian Neumayer, Gabriele Steidl
A common way to quantify the ,,distance'' between measures is via their discrepancy, also known as maximum mean discrepancy (MMD). Discrepancies are related to Sinkhorn divergences…