ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1902.04760
  4. Cited By
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian
  Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation
v1v2v3 (latest)

Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

13 February 2019
Greg Yang
ArXiv (abs)PDFHTML

Papers citing "Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation"

11 / 211 papers shown
On Exact Computation with an Infinitely Wide Neural Net
On Exact Computation with an Infinitely Wide Neural Net
Sanjeev Arora
S. Du
Wei Hu
Zhiyuan Li
Ruslan Salakhutdinov
Ruosong Wang
641
991
0
26 Apr 2019
A Mean Field Theory of Batch Normalization
A Mean Field Theory of Batch Normalization
Greg Yang
Jeffrey Pennington
Vinay Rao
Jascha Narain Sohl-Dickstein
S. Schoenholz
196
185
0
21 Feb 2019
On the Impact of the Activation Function on Deep Neural Networks
  Training
On the Impact of the Activation Function on Deep Neural Networks Training
Soufiane Hayou
Arnaud Doucet
Judith Rousseau
ODL
271
219
0
19 Feb 2019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient
  Descent
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
Jaehoon Lee
Lechao Xiao
S. Schoenholz
Yasaman Bahri
Roman Novak
Jascha Narain Sohl-Dickstein
Jeffrey Pennington
647
1,215
0
18 Feb 2019
Generalization Error Bounds of Gradient Descent for Learning
  Over-parameterized Deep ReLU Networks
Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks
Yuan Cao
Quanquan Gu
ODLMLTAI4CE
597
166
0
04 Feb 2019
Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs
Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs
D. Gilboa
B. Chang
Minmin Chen
Greg Yang
S. Schoenholz
Ed H. Chi
Jeffrey Pennington
225
43
0
25 Jan 2019
A Theoretical Analysis of Deep Q-Learning
A Theoretical Analysis of Deep Q-Learning
Jianqing Fan
Zhuoran Yang
Yuchen Xie
Zhaoran Wang
585
704
0
01 Jan 2019
Learning and Generalization in Overparameterized Neural Networks, Going
  Beyond Two Layers
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
Zeyuan Allen-Zhu
Yuanzhi Li
Yingyu Liang
MLT
840
815
0
12 Nov 2018
Regularization Matters: Generalization and Optimization of Neural Nets
  v.s. their Induced Kernel
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
Colin Wei
Jason D. Lee
Qiang Liu
Tengyu Ma
724
263
0
12 Oct 2018
Bayesian Deep Convolutional Networks with Many Channels are Gaussian
  Processes
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes
Roman Novak
Lechao Xiao
Jaehoon Lee
Yasaman Bahri
Greg Yang
Jiri Hron
Daniel A. Abolafia
Jeffrey Pennington
Jascha Narain Sohl-Dickstein
UQCVBDL
332
324
0
11 Oct 2018
Universal Statistics of Fisher Information in Deep Neural Networks: Mean
  Field Approach
Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach
Ryo Karakida
S. Akaho
S. Amari
FedML
530
164
0
04 Jun 2018
Previous
12345