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. 2101.04243
  4. Cited By
A Convergence Theory Towards Practical Over-parameterized Deep Neural
  Networks

A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks

12 January 2021
Asaf Noy
Yi Tian Xu
Y. Aflalo
Lihi Zelnik-Manor
R. L. Jin
ArXivPDFHTML

Papers citing "A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks"

3 / 3 papers shown
Title
When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work
When Expressivity Meets Trainability: Fewer than nnn Neurons Can Work
Jiawei Zhang
Yushun Zhang
Mingyi Hong
Ruoyu Sun
Z. Luo
21
10
0
21 Oct 2022
Global optimality conditions for deep neural networks
Global optimality conditions for deep neural networks
Chulhee Yun
S. Sra
Ali Jadbabaie
121
117
0
08 Jul 2017
The Loss Surfaces of Multilayer Networks
The Loss Surfaces of Multilayer Networks
A. Choromańska
Mikael Henaff
Michaël Mathieu
Gerard Ben Arous
Yann LeCun
ODL
177
1,185
0
30 Nov 2014
1