Felix Voigtländer

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 24

Top Topics

Rate Distortion

Smooth Functions

Mumford Shah Functional

Top Venues

Found. Comput. Math.

ACM Trans. Parallel Comput.

SIAM J. Math. Data Sci.

Publications

Philipp Grohs, Felix Voigtländer
Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces. Found. Comput. Math. 24 (4) (2024)
Philipp Grohs, Andreas Klotz, Felix Voigtländer
Phase Transitions in Rate Distortion Theory and Deep Learning. Found. Comput. Math. 23 (1) (2023)
Paul Geuchen, Felix Voigtländer
-functions using shallow complex-valued neural networks. CoRR (2023)
Thomas Jahn, Tino Ullrich, Felix Voigtländer
-minimization. J. Complex. 79 (2023)
Paul Geuchen, Thomas Heindl, Dominik Stöger, Felix Voigtländer
On the Lipschitz constant of random neural networks. CoRR (2023)
Julius Berner, Philipp Grohs, Felix Voigtländer
Learning ReLU networks to high uniform accuracy is intractable. ICLR (2023)
Paul Geuchen, Felix Voigtländer
Optimal approximation using complex-valued neural networks. NeurIPS (2023)
Julius Berner, Philipp Grohs, Felix Voigtländer
Training ReLU networks to high uniform accuracy is intractable. CoRR (2022)
Felix Voigtländer
$L^p$ sampling numbers for the Fourier-analytic Barron space. CoRR (2022)
Andrei Caragea, Dae Gwan Lee, Johannes Maly, Götz E. Pfander, Felix Voigtländer
Quantitative Approximation Results for Complex-Valued Neural Networks. SIAM J. Math. Data Sci. 4 (2) (2022)
Thomas Jahn, Tino Ullrich, Felix Voigtländer
-minimization. CoRR (2022)
Philipp Grohs, Felix Voigtländer
Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces. CoRR (2021)
Philipp Grohs, Felix Voigtländer
Sobolev-type embeddings for neural network approximation spaces. CoRR (2021)
Philipp Petersen, Mones Raslan, Felix Voigtländer
Topological Properties of the Set of Functions Generated by Neural Networks of Fixed Size. Found. Comput. Math. 21 (2) (2021)
Philipp Petersen, Felix Voigtländer
Optimal learning of high-dimensional classification problems using deep neural networks. CoRR (2021)
Andrei Caragea, Dae Gwan Lee, Johannes Maly, Götz E. Pfander, Felix Voigtländer
Quantitative approximation results for complex-valued neural networks. CoRR (2021)
Felix Voigtländer
The universal approximation theorem for complex-valued neural networks. CoRR (2020)
Philipp Grohs, Andreas Klotz, Felix Voigtländer
Phase Transitions in Rate Distortion Theory and Deep Learning. CoRR (2020)
Felix Voigtländer, Philipp Petersen
(μ) with deep ReLU neural networks. CoRR (2019)
Rémi Gribonval, Gitta Kutyniok, Morten Nielsen, Felix Voigtländer
Approximation spaces of deep neural networks. CoRR (2019)
Philipp Petersen, Felix Voigtländer
Equivalence of approximation by convolutional neural networks and fully-connected networks. CoRR (2018)
Philipp Petersen, Felix Voigtländer
Optimal approximation of piecewise smooth functions using deep ReLU neural networks. Neural Networks 108 (2018)
Philipp Petersen, Felix Voigtländer
Optimal approximation of piecewise smooth functions using deep ReLU neural networks. CoRR (2017)
David Böhme, Markus Geimer, Lukas Arnold, Felix Voigtländer, Felix Wolf
Identifying the Root Causes of Wait States in Large-Scale Parallel Applications. ACM Trans. Parallel Comput. 3 (2) (2016)
Judit Giménez, Jesús Labarta, F. Xavier Pegenaute, Hui-Fang Wen, David J. Klepacki, I-Hsin Chung, Guojing Cong, Felix Voigtländer, Bernd Mohr
Guided Performance Analysis Combining Profile and Trace Tools. Euro-Par Workshops (2010)
Thomas William, Hartmut Mix, Bernd Mohr, René Menzel, Felix Voigtländer
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. PARCO (2009)