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. 2306.16308
  4. Cited By
Gaussian random field approximation via Stein's method with applications
  to wide random neural networks

Gaussian random field approximation via Stein's method with applications to wide random neural networks

28 June 2023
Krishnakumar Balasubramanian
L. Goldstein
Nathan Ross
Adil Salim
ArXivPDFHTML

Papers citing "Gaussian random field approximation via Stein's method with applications to wide random neural networks"

6 / 6 papers shown
Title
Spectral complexity of deep neural networks
Spectral complexity of deep neural networks
Simmaco Di Lillo
Domenico Marinucci
Michele Salvi
S. Vigogna
BDL
63
1
0
15 May 2024
Quantitative CLTs in Deep Neural Networks
Quantitative CLTs in Deep Neural Networks
Stefano Favaro
Boris Hanin
Domenico Marinucci
I. Nourdin
G. Peccati
BDL
17
10
0
12 Jul 2023
Gaussian Pre-Activations in Neural Networks: Myth or Reality?
Gaussian Pre-Activations in Neural Networks: Myth or Reality?
Pierre Wolinski
Julyan Arbel
AI4CE
65
8
0
24 May 2022
Deep neural networks with dependent weights: Gaussian Process mixture
  limit, heavy tails, sparsity and compressibility
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility
Hoileong Lee
Fadhel Ayed
Paul Jung
Juho Lee
Hongseok Yang
François Caron
25
10
0
17 May 2022
Non-asymptotic approximations of neural networks by Gaussian processes
Non-asymptotic approximations of neural networks by Gaussian processes
Ronen Eldan
Dan Mikulincer
T. Schramm
22
24
0
17 Feb 2021
Trainability and Accuracy of Neural Networks: An Interacting Particle
  System Approach
Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach
Grant M. Rotskoff
Eric Vanden-Eijnden
59
114
0
02 May 2018
1