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20232026
most citedOptimal approximation using complex-valued neural networks

3 citations · 4 across the 5 of their papers we have counts for

collaborators

6 papers

hep-th2026

Generalized Symmetries of QED on Non-Spin Manifolds

Michele Del Zotto, Vivek Saxena

Generalized symmetries are sensitive to the tangential structure of spacetime. We study this interplay for massless QED on oriented non-spin four-manifolds, where the photon become…

stat.ML2025

Optimal estimates for the -Lipschitz constants of deep random ReLU neural networks

Sjoerd Dirksen, Patrick Finke, Paul Geuchen +2

This paper studies the -Lipschitz constants of ReLU neural networks with random parameters for . The distribution of the…

math.FA2024

On best approximation by multivariate ridge functions with applications to generalized translation networks

Paul Geuchen, Palina Salanevich, Olov Schavemaker +1

In this paper, we prove sharp upper and lower bounds for the approximation of Sobolev functions by sums of multivariate ridge functions, i.e., for approximation by functions of the…

stat.ML2023

Upper and lower bounds for the Lipschitz constant of random neural networks

Paul Geuchen, Dominik Stöger, Thomas Telaar +1

Empirical studies have widely demonstrated that neural networks are highly sensitive to small, adversarial perturbations of the input. The worst-case robustness against these so-ca…

math.FA2023★ 1 cited

Universal approximation with complex-valued deep narrow neural networks

Paul Geuchen, Thomas Jahn, Hannes Matt

We study the universality of complex-valued neural networks with bounded widths and arbitrary depths. Under mild assumptions, we give a full description of those activation functio…

math.FA2023★ 3 cited

Optimal approximation using complex-valued neural networks

Paul Geuchen, Felix Voigtlaender

Complex-valued neural networks (CVNNs) have recently shown promising empirical success, for instance for increasing the stability of recurrent neural networks and for improving the…