The universal approximation power of finite-width deep ReLU networks

The universal approximation power of finite-width deep ReLU networks

5 June 2018

Dmytro Perekrestenko

Dennis Elbrächter

Helmut Bölcskei

Papers citing "The universal approximation power of finite-width deep ReLU networks"

10 / 10 papers shown

Title
Training Neural Networks Using Reproducing Kernel Space Interpolation and Model Reduction Eric A. Werneburg 21 0 0 31 Aug 2023
Data Topology-Dependent Upper Bounds of Neural Network Widths Sangmin Lee Jong Chul Ye 28 0 0 25 May 2023
Looped Transformers as Programmable Computers Angeliki Giannou Shashank Rajput Jy-yong Sohn Kangwook Lee Jason D. Lee Dimitris Papailiopoulos 10 96 0 30 Jan 2023
Seeking Interpretability and Explainability in Binary Activated Neural Networks Benjamin Leblanc Pascal Germain FAtt 42 1 0 07 Sep 2022
Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation Arnulf Jentzen Timo Welti 22 15 0 03 Mar 2020
Growing axons: greedy learning of neural networks with application to function approximation Daria Fokina Ivan Oseledets 24 18 0 28 Oct 2019
The phase diagram of approximation rates for deep neural networks Dmitry Yarotsky Anton Zhevnerchuk 30 121 0 22 Jun 2019
The Oracle of DLphi Dominik Alfke W. Baines J. Blechschmidt Mauricio J. del Razo Sarmina Amnon Drory ... L. Thesing Philipp Trunschke Johannes von Lindheim David Weber Melanie Weber 39 0 0 17 Jan 2019
A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients Arnulf Jentzen Diyora Salimova Timo Welti AI4CE 24 116 0 19 Sep 2018
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations Philipp Grohs F. Hornung Arnulf Jentzen Philippe von Wurstemberger 16 167 0 07 Sep 2018