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Neural Network Approximation: Three Hidden Layers Are Enough

Neural Network Approximation: Three Hidden Layers Are Enough

25 October 2020
Zuowei Shen
Haizhao Yang
Shijun Zhang
ArXivPDFHTML

Papers citing "Neural Network Approximation: Three Hidden Layers Are Enough"

16 / 16 papers shown
Title
From Equations to Insights: Unraveling Symbolic Structures in PDEs with LLMs
Rohan Bhatnagar
Ling Liang
Krish Patel
Haizhao Yang
36
0
0
13 Mar 2025
Don't Fear Peculiar Activation Functions: EUAF and Beyond
Don't Fear Peculiar Activation Functions: EUAF and Beyond
Qianchao Wang
Shijun Zhang
Dong Zeng
Zhaoheng Xie
Hengtao Guo
Feng-Lei Fan
Tieyong Zeng
34
3
0
12 Jul 2024
On the Generalization and Approximation Capacities of Neural Controlled
  Differential Equations
On the Generalization and Approximation Capacities of Neural Controlled Differential Equations
Linus Bleistein
Agathe Guilloux
30
1
0
26 May 2023
Universal Approximation Property of Hamiltonian Deep Neural Networks
Universal Approximation Property of Hamiltonian Deep Neural Networks
M. Zakwan
M. d’Angelo
Giancarlo Ferrari-Trecate
28
5
0
21 Mar 2023
Deep Neural Network Approximation of Invariant Functions through
  Dynamical Systems
Deep Neural Network Approximation of Invariant Functions through Dynamical Systems
Qianxiao Li
T. Lin
Zuowei Shen
15
6
0
18 Aug 2022
Finite Expression Method for Solving High-Dimensional Partial
  Differential Equations
Finite Expression Method for Solving High-Dimensional Partial Differential Equations
Senwei Liang
Haizhao Yang
21
18
0
21 Jun 2022
A Note on Machine Learning Approach for Computational Imaging
A Note on Machine Learning Approach for Computational Imaging
Bin Dong
10
0
0
24 Feb 2022
Stochastic Causal Programming for Bounding Treatment Effects
Stochastic Causal Programming for Bounding Treatment Effects
Kirtan Padh
Jakob Zeitler
David S. Watson
Matt J. Kusner
Ricardo M. A. Silva
Niki Kilbertus
CML
20
26
0
22 Feb 2022
Designing Universal Causal Deep Learning Models: The Geometric
  (Hyper)Transformer
Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer
Beatrice Acciaio
Anastasis Kratsios
G. Pammer
OOD
39
20
0
31 Jan 2022
Deep Nonparametric Estimation of Operators between Infinite Dimensional
  Spaces
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces
Hao Liu
Haizhao Yang
Minshuo Chen
T. Zhao
Wenjing Liao
32
36
0
01 Jan 2022
The Discovery of Dynamics via Linear Multistep Methods and Deep
  Learning: Error Estimation
The Discovery of Dynamics via Linear Multistep Methods and Deep Learning: Error Estimation
Q. Du
Yiqi Gu
Haizhao Yang
Chao Zhou
24
20
0
21 Mar 2021
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth
Zuowei Shen
Haizhao Yang
Shijun Zhang
101
115
0
28 Feb 2021
Reproducing Activation Function for Deep Learning
Reproducing Activation Function for Deep Learning
Senwei Liang
Liyao Lyu
Chunmei Wang
Haizhao Yang
28
21
0
13 Jan 2021
Friedrichs Learning: Weak Solutions of Partial Differential Equations
  via Deep Learning
Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning
Fan Chen
J. Huang
Chunmei Wang
Haizhao Yang
15
30
0
15 Dec 2020
The Kolmogorov-Arnold representation theorem revisited
The Kolmogorov-Arnold representation theorem revisited
Johannes Schmidt-Hieber
28
125
0
31 Jul 2020
Two-Layer Neural Networks for Partial Differential Equations:
  Optimization and Generalization Theory
Two-Layer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory
Tao Luo
Haizhao Yang
11
73
0
28 Jun 2020
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