On the Approximation Power of Two-Layer Networks of Random ReLUs

3 February 2021

Papers citing "On the Approximation Power of Two-Layer Networks of Random ReLUs"

10 / 10 papers shown

Title
On the Generalization Properties of Diffusion Models Puheng Li Zhong Li Huishuai Zhang Jiang Bian 74 29 0 13 Mar 2025
ScoreFusion: Fusing Score-based Generative Models via Kullback-Leibler Barycenters Hao Liu Junze Tony Ye Ye Jose H. Blanchet DiffM FedML 36 1 0 28 Jun 2024
Randomly Initialized One-Layer Neural Networks Make Data Linearly Separable Promit Ghosal Srinath Mahankali Yihang Sun MLT 29 4 0 24 May 2022
Optimization-Based Separations for Neural Networks Itay Safran Jason D. Lee 185 14 0 04 Dec 2021
Quantum machine learning beyond kernel methods Sofiene Jerbi Lukas J. Fiderer Hendrik Poulsen Nautrup Jonas M. Kubler H. Briegel Vedran Dunjko 16 161 0 25 Oct 2021
Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem Clayton Sanford Vaggos Chatziafratis 16 1 0 19 Oct 2021
Neural Active Learning with Performance Guarantees Pranjal Awasthi Christoph Dann Claudio Gentile Ayush Sekhari Zhilei Wang 29 22 0 06 Jun 2021
Depth separation beyond radial functions Luca Venturi Samy Jelassi Tristan Ozuch Joan Bruna 19 15 0 02 Feb 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