Enhanced Performance Analysis of Multi-core Applications with an Integrated Tool-chain - Using Scalasca and Vampir to Optimise the Metal Forming Simulation FE Software INDEED. - Thomas William; Hartmut Mix; Bernd Mohr; René Menzel; Felix Voigtländer (PARCO, 2009)

Guided Performance Analysis Combining Profile and Trace Tools. - Judit Giménez; Jesús Labarta; F. Xavier Pegenaute; Hui-Fang Wen; David J. Klepacki; I-Hsin Chung; Guojing Cong; Felix Voigtländer; Bernd Mohr (Euro-Par Workshops, 2010)

Identifying the Root Causes of Wait States in Large-Scale Parallel Applications. - David Böhme; Markus Geimer; Lukas Arnold; Felix Voigtländer; Felix Wolf 001 (ACM Trans. Parallel Comput., 2016)

Equivalence of approximation by convolutional neural networks and fully-connected networks. - Philipp Petersen; Felix Voigtländer (CoRR, 2018)

Optimal approximation of piecewise smooth functions using deep ReLU neural networks. - Philipp Petersen; Felix Voigtländer (Neural Networks, 2018)

Approximation in Lp(μ) with deep ReLU neural networks. - Felix Voigtländer; Philipp Petersen (CoRR, 2019)

Approximation spaces of deep neural networks. - Rémi Gribonval; Gitta Kutyniok; Morten Nielsen 002; Felix Voigtländer (CoRR, 2019)

The universal approximation theorem for complex-valued neural networks. - Felix Voigtländer (CoRR, 2020)

Sobolev-type embeddings for neural network approximation spaces. - Philipp Grohs; Felix Voigtländer (CoRR, 2021)

Optimal learning of high-dimensional classification problems using deep neural networks. - Philipp Petersen; Felix Voigtländer (CoRR, 2021)

Topological Properties of the Set of Functions Generated by Neural Networks of Fixed Size. - Philipp Petersen; Mones Raslan; Felix Voigtländer (Found. Comput. Math., 2021)

Training ReLU networks to high uniform accuracy is intractable. - Julius Berner; Philipp Grohs; Felix Voigtländer (CoRR, 2022)

$L^p$ sampling numbers for the Fourier-analytic Barron space. - Felix Voigtländer (CoRR, 2022)

Sampling numbers of smoothness classes via 𝓁1-minimization. - Thomas Jahn; Tino Ullrich; Felix Voigtländer (CoRR, 2022)

Quantitative Approximation Results for Complex-Valued Neural Networks. - Andrei Caragea; Dae Gwan Lee; Johannes Maly; Götz E. Pfander; Felix Voigtländer (SIAM J. Math. Data Sci., 2022)

Learning ReLU networks to high uniform accuracy is intractable. - Julius Berner; Philipp Grohs; Felix Voigtländer (ICLR, 2023)

Optimal approximation using complex-valued neural networks. - Paul Geuchen; Felix Voigtländer (NeurIPS, 2023)

Optimal approximation of Ck-functions using shallow complex-valued neural networks. - Paul Geuchen; Felix Voigtländer (CoRR, 2023)

Upper and lower bounds for the Lipschitz constant of random neural networks. - Paul Geuchen; Thomas Heindl; Dominik Stöger; Felix Voigtländer (CoRR, 2023)

Phase Transitions in Rate Distortion Theory and Deep Learning. - Philipp Grohs; Andreas Klotz; Felix Voigtländer (Found. Comput. Math., 2023)

Sampling numbers of smoothness classes via ℓ1-minimization. - Thomas Jahn; Tino Ullrich; Felix Voigtländer (J. Complex., 2023)

On best approximation by multivariate ridge functions with applications to generalized translation networks. - Paul Geuchen; Palina Salanevich; Olov Schavemaker; Felix Voigtländer (CoRR, 2024)

Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces. - Philipp Grohs; Felix Voigtländer (Found. Comput. Math., 2024)

Near-optimal estimates for the ℓp-Lipschitz constants of deep random ReLU neural networks. - Sjoerd Dirksen; Patrick Finke; Paul Geuchen; Dominik Stöger; Felix Voigtländer (CoRR, 2025)