Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions?

24 April 2022

Papers citing "Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions?"

6 / 6 papers shown

Title
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts Anastasis Kratsios Haitz Sáez de Ocáriz Borde Takashi Furuya Marc T. Law MoE 41 1 0 05 Feb 2024
Data Topology-Dependent Upper Bounds of Neural Network Widths Sangmin Lee Jong Chul Ye 26 0 0 25 May 2023
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis Luca Galimberti Anastasis Kratsios Giulia Livieri OOD 28 14 0 24 Oct 2022
Universal Approximation Under Constraints is Possible with Transformers Anastasis Kratsios Behnoosh Zamanlooy Tianlin Liu Ivan Dokmanić 53 26 0 07 Oct 2021
Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping James Martens Andy Ballard Guillaume Desjardins G. Swirszcz Valentin Dalibard Jascha Narain Sohl-Dickstein S. Schoenholz 88 43 0 05 Oct 2021
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth Zuowei Shen Haizhao Yang Shijun Zhang 101 115 0 28 Feb 2021