Felix Voigtländer

According to our database, Felix Voigtländer authored at least 22 papers between 2009 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Sampling numbers of smoothness classes via <i>ℓ</i>¹-minimization.

Thomas Jahn, Tino Ullrich, Felix Voigtländer

J. Complex., December, 2023

Phase Transitions in Rate Distortion Theory and Deep Learning.

Philipp Grohs, Andreas Klotz, Felix Voigtländer

Found. Comput. Math., February, 2023

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

Paul Geuchen, Thomas Heindl, Dominik Stöger, Felix Voigtländer

CoRR, 2023

Optimal approximation of C^k-functions using shallow complex-valued neural networks.

Paul Geuchen, Felix Voigtländer

CoRR, 2023

Optimal approximation using complex-valued neural networks.

Paul Geuchen, Felix Voigtländer

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning ReLU networks to high uniform accuracy is intractable.

Julius Berner, Philipp Grohs, Felix Voigtländer

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

Sampling numbers of smoothness classes via 𝓁¹-minimization.

Thomas Jahn, Tino Ullrich, Felix Voigtländer

CoRR, 2022

$L^p$ sampling numbers for the Fourier-analytic Barron space.

Felix Voigtländer

CoRR, 2022

Training ReLU networks to high uniform accuracy is intractable.

Julius Berner, Philipp Grohs, Felix Voigtländer

CoRR, 2022

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

Optimal learning of high-dimensional classification problems using deep neural networks.

Philipp Petersen, Felix Voigtländer

CoRR, 2021

Sobolev-type embeddings for neural network approximation spaces.

Philipp Grohs, Felix Voigtländer

CoRR, 2021

Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces.

Philipp Grohs, Felix Voigtländer

CoRR, 2021

2020

The universal approximation theorem for complex-valued neural networks.

Felix Voigtländer

CoRR, 2020

2019

Approximation spaces of deep neural networks.

Rémi Gribonval, Gitta Kutyniok, Morten Nielsen, Felix Voigtländer

CoRR, 2019

Approximation in L^p(μ) with deep ReLU neural networks.

Felix Voigtländer, Philipp Petersen

CoRR, 2019

2018

Optimal approximation of piecewise smooth functions using deep ReLU neural networks.

Philipp Petersen, Felix Voigtländer

Neural Networks, 2018

Equivalence of approximation by convolutional neural networks and fully-connected networks.

Philipp Petersen, Felix Voigtländer

CoRR, 2018

2016

Identifying the Root Causes of Wait States in Large-Scale Parallel Applications.

David Böhme, Markus Geimer, Lukas Arnold, Felix Voigtländer, Felix Wolf

ACM Trans. Parallel Comput., 2016

2010

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

Proceedings of the Euro-Par 2010 Parallel Processing Workshops, 2010

2009

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

Proceedings of the Parallel Computing: From Multicores and GPU's to Petascale, 2009