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Achieve the Minimum Width of Neural Networks for Universal Approximation
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Achieve the Minimum Width of Neural Networks for Universal Approximation

Yongqiang Cai
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Papers citing "Achieve the Minimum Width of Neural Networks for Universal Approximation"

16 / 16 papers shown
Title
Addressing common misinterpretations of KART and UAT in neural network literature
Addressing common misinterpretations of KART and UAT in neural network literature
Vugar Ismailov
HAI
137
1
0
29 Aug 2024
Neural Networks Trained by Weight Permutation are Universal Approximators
Neural Networks Trained by Weight Permutation are Universal Approximators
Yongqiang Cai
Gaohang Chen
Zhonghua Qiao
191
1
0
01 Jul 2024
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
177
118
0
28 Feb 2021
Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
Paulo Tabuada
Bahman Gharesifard
104
26
0
12 Jul 2020
Minimum Width for Universal Approximation
Minimum Width for Universal Approximation
Sejun Park
Chulhee Yun
Jaeho Lee
Jinwoo Shin
75
125
0
16 Jun 2020
Deep Learning via Dynamical Systems: An Approximation Perspective
Deep Learning via Dynamical Systems: An Approximation Perspective
Qianxiao Li
Ting Lin
Zuowei Shen
AI4TSAI4CE
98
109
0
22 Dec 2019
The phase diagram of approximation rates for deep neural networks
The phase diagram of approximation rates for deep neural networks
Dmitry Yarotsky
Anton Zhevnerchuk
76
122
0
22 Jun 2019
Universal Approximation with Deep Narrow Networks
Universal Approximation with Deep Narrow Networks
Patrick Kidger
Terry Lyons
136
335
0
21 May 2019
Deep, Skinny Neural Networks are not Universal Approximators
Deep, Skinny Neural Networks are not Universal Approximators
Jesse Johnson
57
67
0
30 Sep 2018
Neural Ordinary Differential Equations
Neural Ordinary Differential Equations
T. Chen
Yulia Rubanova
J. Bettencourt
David Duvenaud
AI4CE
457
5,176
0
19 Jun 2018
Approximating Continuous Functions by ReLU Nets of Minimal Width
Approximating Continuous Functions by ReLU Nets of Minimal Width
Boris Hanin
Mark Sellke
107
239
0
31 Oct 2017
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
Yiping Lu
Aoxiao Zhong
Quanzheng Li
Bin Dong
210
505
0
27 Oct 2017
The Expressive Power of Neural Networks: A View from the Width
The Expressive Power of Neural Networks: A View from the Width
Zhou Lu
Hongming Pu
Feicheng Wang
Zhiqiang Hu
Liwei Wang
99
899
0
08 Sep 2017
Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations
Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations
Boris Hanin
73
356
0
09 Aug 2017
Benefits of depth in neural networks
Benefits of depth in neural networks
Matus Telgarsky
380
609
0
14 Feb 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.3K
194,641
0
10 Dec 2015