ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2004.00245
  4. Cited By
Depth Selection for Deep ReLU Nets in Feature Extraction and
  Generalization

Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization

1 April 2020
Zhi Han
Siquan Yu
Shao-Bo Lin
Ding-Xuan Zhou
    OOD
ArXiv (abs)PDFHTML

Papers citing "Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization"

19 / 19 papers shown
Title
Deep Neural Networks for Rotation-Invariance Approximation and Learning
Deep Neural Networks for Rotation-Invariance Approximation and Learning
C. Chui
Shao-Bo Lin
Ding-Xuan Zhou
158
34
0
03 Apr 2019
Realizing data features by deep nets
Realizing data features by deep nets
Zheng-Chu Guo
Lei Shi
Shao-Bo Lin
28
20
0
01 Jan 2019
A Convergence Theory for Deep Learning via Over-Parameterization
A Convergence Theory for Deep Learning via Over-Parameterization
Zeyuan Allen-Zhu
Yuanzhi Li
Zhao Song
AI4CEODL
266
1,469
0
09 Nov 2018
Universality of Deep Convolutional Neural Networks
Universality of Deep Convolutional Neural Networks
Ding-Xuan Zhou
HAIPINN
412
517
0
28 May 2018
Generalization and Expressivity for Deep Nets
Generalization and Expressivity for Deep Nets
Shao-Bo Lin
59
45
0
10 Mar 2018
Deep Neural Networks Learn Non-Smooth Functions Effectively
Deep Neural Networks Learn Non-Smooth Functions Effectively
Masaaki Imaizumi
Kenji Fukumizu
150
124
0
13 Feb 2018
Optimal approximation of piecewise smooth functions using deep ReLU
  neural networks
Optimal approximation of piecewise smooth functions using deep ReLU neural networks
P. Petersen
Felix Voigtländer
223
475
0
15 Sep 2017
A Deep Neural Network to identify foreshocks in real time
A Deep Neural Network to identify foreshocks in real time
K. Vikraman
46
8
0
26 Nov 2016
Depth-Width Tradeoffs in Approximating Natural Functions with Neural
  Networks
Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks
Itay Safran
Ohad Shamir
87
175
0
31 Oct 2016
Error bounds for approximations with deep ReLU networks
Error bounds for approximations with deep ReLU networks
Dmitry Yarotsky
198
1,233
0
03 Oct 2016
Why does deep and cheap learning work so well?
Why does deep and cheap learning work so well?
Henry W. Lin
Max Tegmark
David Rolnick
85
610
0
29 Aug 2016
Deep vs. shallow networks : An approximation theory perspective
Deep vs. shallow networks : An approximation theory perspective
H. Mhaskar
T. Poggio
165
341
0
10 Aug 2016
Constructive neural network learning
Constructive neural network learning
Shaobo Lin
Jinshan Zeng
Xiaoqin Zhang
66
31
0
30 Apr 2016
The Power of Depth for Feedforward Neural Networks
The Power of Depth for Feedforward Neural Networks
Ronen Eldan
Ohad Shamir
221
732
0
12 Dec 2015
Provable approximation properties for deep neural networks
Provable approximation properties for deep neural networks
Uri Shaham
A. Cloninger
Ronald R. Coifman
181
231
0
24 Sep 2015
Unregularized Online Learning Algorithms with General Loss Functions
Unregularized Online Learning Algorithms with General Loss Functions
Yiming Ying
Ding-Xuan Zhou
72
55
0
02 Mar 2015
On the Number of Linear Regions of Deep Neural Networks
On the Number of Linear Regions of Deep Neural Networks
Guido Montúfar
Razvan Pascanu
Kyunghyun Cho
Yoshua Bengio
96
1,256
0
08 Feb 2014
Representation Learning: A Review and New Perspectives
Representation Learning: A Review and New Perspectives
Yoshua Bengio
Aaron Courville
Pascal Vincent
OODSSL
278
12,460
0
24 Jun 2012
Invariant Scattering Convolution Networks
Invariant Scattering Convolution Networks
Joan Bruna
S. Mallat
131
1,279
0
05 Mar 2012
1