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20162026
most citedProof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces

6 citations · 19 across the 22 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

math.FA2020

The universal approximation theorem for complex-valued neural networks

Felix Voigtlaender

We generalize the classical universal approximation theorem for neural networks to the case of complex-valued neural networks. Precisely, we consider feedforward networks with a co…

math.FA2020

Neural network approximation and estimation of classifiers with classification boundary in a Barron class

Andrei Caragea, Philipp Petersen, Felix Voigtlaender

We prove bounds for the approximation and estimation of certain binary classification functions using ReLU neural networks. Our estimation bounds provide a priori performance guara…

math.FA2020

Phase Transitions in Rate Distortion Theory and Deep Learning

Philipp Grohs, Andreas Klotz, Felix Voigtlaender

Rate distortion theory is concerned with optimally encoding a given signal class using a budget of bits, as . We say that can be compres…

math.FA2020

Schur-type Banach modules of integral kernels acting on mixed-norm Lebesgue spaces

Nicki Holighaus, Felix Voigtlaender

Schur's test states that if satisfies and , then the associated integral operator acts boundedly…

math.FA2020

On dual molecules and convolution-dominated operators

José Luis Romero, Jordy Timo van Velthoven, Felix Voigtlaender

We show that sampling or interpolation formulas in reproducing kernel Hilbert spaces can be obtained by reproducing kernels whose dual systems form molecules, ensuring that the siz…