7 citations · 17 across the 5 of their papers we have counts for
5 papers
Hypergraph -Laplacians and Scale Spaces
Ariane Fazeny, Daniel Tenbrinck, Kseniia Lukin +1
This paper introduces gradient, adjoint, and -Laplacian definitions for oriented hypergraphs as well as differential and averaging operators for unoriented hypergraphs. These de…
Resolution-Invariant Image Classification based on Fourier Neural Operators
Samira Kabri, Tim Roith, Daniel Tenbrinck +1
In this paper we investigate the use of Fourier Neural Operators (FNOs) for image classification in comparison to standard Convolutional Neural Networks (CNNs). Neural operators ar…
Spectral Total-Variation Processing of Shapes: Theory and Applications
Jonathan Brokman, Martin Burger, Guy Gilboa
We present an analysis of total-variation (TV) on non-Euclidean parameterized surfaces, a natural representation of the shapes used in 3D graphics. Our work explains recent experim…
Neural Architecture Search via Bregman Iterations
Leon Bungert, Tim Roith, Daniel Tenbrinck +1
We propose a novel strategy for Neural Architecture Search (NAS) based on Bregman iterations. Starting from a sparse neural network our gradient-based one-shot algorithm gradually…
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks
Leo Schwinn, An Nguyen, René Raab +4
The vulnerability of deep neural networks to small and even imperceptible perturbations has become a central topic in deep learning research. Although several sophisticated defense…