A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks

9 June 2022

Papers citing "A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks"

8 / 8 papers shown

Title
Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective Samuel Lanthaler 39 3 0 26 Jun 2024
Operator Learning: Algorithms and Analysis Nikola B. Kovachki S. Lanthaler Andrew M. Stuart 40 22 0 24 Feb 2024
Optimal rates of approximation by shallow ReLU $^k$ neural networks and applications to nonparametric regression Yunfei Yang Ding-Xuan Zhou 34 19 0 04 Apr 2023
Operator learning with PCA-Net: upper and lower complexity bounds S. Lanthaler 21 25 0 28 Mar 2023
Limitations on approximation by deep and shallow neural networks G. Petrova P. Wojtaszczyk 11 7 0 30 Nov 2022
Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces Jonathan W. Siegel 20 28 0 25 Nov 2022
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth Zuowei Shen Haizhao Yang Shijun Zhang 101 115 0 28 Feb 2021
Benefits of depth in neural networks Matus Telgarsky 133 602 0 14 Feb 2016