4 citations · 4 across the 2 of their papers we have counts for
3 papers
math.NA2022★ 4 cited
Sampling numbers of smoothness classes via -minimization
Thomas Jahn, Tino Ullrich, Felix Voigtlaender
Using techniques developed recently in the field of compressed sensing we prove new upper bounds for general (nonlinear) sampling numbers of (quasi-)Banach smoothness spaces in $L^…
math.FA2021
Quantitative approximation results for complex-valued neural networks
A. Caragea, D. G. Lee, J. Maly +2
Until recently, applications of neural networks in machine learning have almost exclusively relied on real-valued networks. It was recently observed, however, that complex-valued n…
math.FA2017
Optimal approximation of piecewise smooth functions using deep ReLU neural networks
Philipp Petersen, Felix Voigtlaender
We study the necessary and sufficient complexity of ReLU neural networks---in terms of depth and number of weights---which is required for approximating classifier functions in $L^…