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Nonlinear Approximation via Compositions

Nonlinear Approximation via Compositions

26 February 2019
Zuowei Shen
Haizhao Yang
Shijun Zhang
ArXivPDFHTML

Papers citing "Nonlinear Approximation via Compositions"

17 / 17 papers shown
Title
Deep learning with missing data
Deep learning with missing data
Tianyi Ma
Tengyao Wang
R. Samworth
66
0
0
21 Apr 2025
Inferring Outcome Means of Exponential Family Distributions Estimated by Deep Neural Networks
Inferring Outcome Means of Exponential Family Distributions Estimated by Deep Neural Networks
Xuran Meng
Yi Li
BDL
32
0
0
12 Apr 2025
Higher Order Approximation Rates for ReLU CNNs in Korobov Spaces
Higher Order Approximation Rates for ReLU CNNs in Korobov Spaces
Yuwen Li
Guozhi Zhang
46
1
0
20 Jan 2025
On the optimal approximation of Sobolev and Besov functions using deep
  ReLU neural networks
On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks
Yunfei Yang
62
2
0
02 Sep 2024
Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural
  Network Derivatives
Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives
Yahong Yang
Haizhao Yang
Yang Xiang
31
19
0
15 May 2023
Approximation results for Gradient Descent trained Shallow Neural
  Networks in $1d$
Approximation results for Gradient Descent trained Shallow Neural Networks in 1d1d1d
R. Gentile
G. Welper
ODL
52
6
0
17 Sep 2022
Robust Nonparametric Regression with Deep Neural Networks
Robust Nonparametric Regression with Deep Neural Networks
Guohao Shen
Yuling Jiao
Yuanyuan Lin
Jian Huang
OOD
33
13
0
21 Jul 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
Neural Network Approximation: Three Hidden Layers Are Enough
Neural Network Approximation: Three Hidden Layers Are Enough
Zuowei Shen
Haizhao Yang
Shijun Zhang
30
115
0
25 Oct 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
32
73
0
28 Jun 2020
Approximation in shift-invariant spaces with deep ReLU neural networks
Approximation in shift-invariant spaces with deep ReLU neural networks
Yunfei Yang
Zhen Li
Yang Wang
34
14
0
25 May 2020
AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with
  Swarm Intelligence
AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence
Rohan Mohapatra
Snehanshu Saha
C. Coello
Anwesh Bhattacharya
S. Dhavala
S. Saha
ODL
18
21
0
19 May 2020
Deep Network Approximation for Smooth Functions
Deep Network Approximation for Smooth Functions
Jianfeng Lu
Zuowei Shen
Haizhao Yang
Shijun Zhang
67
247
0
09 Jan 2020
Deep Learning via Dynamical Systems: An Approximation Perspective
Deep Learning via Dynamical Systems: An Approximation Perspective
Qianxiao Li
Ting Lin
Zuowei Shen
AI4TS
AI4CE
19
107
0
22 Dec 2019
Variational Physics-Informed Neural Networks For Solving Partial
  Differential Equations
Variational Physics-Informed Neural Networks For Solving Partial Differential Equations
E. Kharazmi
Z. Zhang
George Karniadakis
21
236
0
27 Nov 2019
A Review on Deep Learning in Medical Image Reconstruction
A Review on Deep Learning in Medical Image Reconstruction
Hai-Miao Zhang
Bin Dong
MedIm
35
122
0
23 Jun 2019
Deep Network Approximation Characterized by Number of Neurons
Deep Network Approximation Characterized by Number of Neurons
Zuowei Shen
Haizhao Yang
Shijun Zhang
20
182
0
13 Jun 2019
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