Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth

7 June 2020

Papers citing "Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth"

7 / 7 papers shown

Title
Approximation results for Gradient Descent trained Shallow Neural Networks in $1d$ R. Gentile G. Welper ODL 56 6 0 17 Sep 2022
Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem Clayton Sanford Vaggos Chatziafratis 16 1 0 19 Oct 2021
On the Representation of Solutions to Elliptic PDEs in Barron Spaces Ziang Chen Jianfeng Lu Yulong Lu 38 27 0 14 Jun 2021
Depth separation beyond radial functions Luca Venturi Samy Jelassi Tristan Ozuch Joan Bruna 19 15 0 02 Feb 2021
Size and Depth Separation in Approximating Benign Functions with Neural Networks Gal Vardi Daniel Reichman T. Pitassi Ohad Shamir 28 7 0 30 Jan 2021
$Approximation by Combinations of ReLU and Squared ReLU Ridge Functions with $ \ell^1 $ and $ \ell^0 $ Controls$ Approximation by Combinations of ReLU and Squared ReLU Ridge Functions with $\ell^1$ and $\ell^0$ Controls Jason M. Klusowski Andrew R. Barron 132 142 0 26 Jul 2016
Benefits of depth in neural networks Matus Telgarsky 153 603 0 14 Feb 2016