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. 2212.01744
  4. Cited By
Statistical Physics of Deep Neural Networks: Initialization toward
  Optimal Channels

Statistical Physics of Deep Neural Networks: Initialization toward Optimal Channels

4 December 2022
Kangyu Weng
Aohua Cheng
Ziyang Zhang
Pei Sun
Yang Tian
ArXivPDFHTML

Papers citing "Statistical Physics of Deep Neural Networks: Initialization toward Optimal Channels"

40 / 40 papers shown
Title
Neural Tangent Kernel: A Survey
Neural Tangent Kernel: A Survey
Eugene Golikov
Eduard Pokonechnyy
Vladimir Korviakov
48
14
0
29 Aug 2022
Nondeterminism and Instability in Neural Network Optimization
Nondeterminism and Instability in Neural Network Optimization
Cecilia Summers
M. Dinneen
52
40
0
08 Mar 2021
Statistical Mechanics of Deep Linear Neural Networks: The
  Back-Propagating Kernel Renormalization
Statistical Mechanics of Deep Linear Neural Networks: The Back-Propagating Kernel Renormalization
Qianyi Li
H. Sompolinsky
131
72
0
07 Dec 2020
Learning from Noisy Labels with Deep Neural Networks: A Survey
Learning from Noisy Labels with Deep Neural Networks: A Survey
Hwanjun Song
Minseok Kim
Dongmin Park
Yooju Shin
Jae-Gil Lee
NoLa
92
985
0
16 Jul 2020
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any
  Architecture are Gaussian Processes
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
Greg Yang
105
198
0
28 Oct 2019
On Mutual Information Maximization for Representation Learning
On Mutual Information Maximization for Representation Learning
Michael Tschannen
Josip Djolonga
Paul Kishan Rubenstein
Sylvain Gelly
Mario Lucic
SSL
156
493
0
31 Jul 2019
Contrastive Multiview Coding
Contrastive Multiview Coding
Yonglong Tian
Dilip Krishnan
Phillip Isola
SSL
153
2,395
0
13 Jun 2019
Data-Efficient Image Recognition with Contrastive Predictive Coding
Data-Efficient Image Recognition with Contrastive Predictive Coding
Olivier J. Hénaff
A. Srinivas
J. Fauw
Ali Razavi
Carl Doersch
S. M. Ali Eslami
Aaron van den Oord
SSL
111
1,427
0
22 May 2019
On Variational Bounds of Mutual Information
On Variational Bounds of Mutual Information
Ben Poole
Sherjil Ozair
Aaron van den Oord
Alexander A. Alemi
George Tucker
SSL
93
806
0
16 May 2019
Nonparametric Density Estimation for High-Dimensional Data - Algorithms
  and Applications
Nonparametric Density Estimation for High-Dimensional Data - Algorithms and Applications
Zhipeng Wang
D. W. Scott
54
69
0
30 Mar 2019
Mean Field Analysis of Deep Neural Networks
Mean Field Analysis of Deep Neural Networks
Justin A. Sirignano
K. Spiliopoulos
54
82
0
11 Mar 2019
A Theoretical Analysis of Contrastive Unsupervised Representation
  Learning
A Theoretical Analysis of Contrastive Unsupervised Representation Learning
Sanjeev Arora
H. Khandeparkar
M. Khodak
Orestis Plevrakis
Nikunj Saunshi
SSL
96
776
0
25 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
188
1,097
0
18 Feb 2019
Mean-field theory of two-layers neural networks: dimension-free bounds
  and kernel limit
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit
Song Mei
Theodor Misiakiewicz
Andrea Montanari
MLT
68
278
0
16 Feb 2019
Mean Field Limit of the Learning Dynamics of Multilayer Neural Networks
Mean Field Limit of the Learning Dynamics of Multilayer Neural Networks
Phan-Minh Nguyen
AI4CE
44
72
0
07 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
68
42
0
25 Jan 2019
On Lazy Training in Differentiable Programming
On Lazy Training in Differentiable Programming
Lénaïc Chizat
Edouard Oyallon
Francis R. Bach
100
833
0
19 Dec 2018
Formal Limitations on the Measurement of Mutual Information
Formal Limitations on the Measurement of Mutual Information
David A. McAllester
K. Stratos
SSL
63
275
0
10 Nov 2018
Deep Metric Learning with Hierarchical Triplet Loss
Deep Metric Learning with Hierarchical Triplet Loss
Weifeng Ge
Weilin Huang
Dengke Dong
Matthew R. Scott
154
413
0
16 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
UQCV
BDL
54
309
0
11 Oct 2018
Learning deep representations by mutual information estimation and
  maximization
Learning deep representations by mutual information estimation and maximization
R. Devon Hjelm
A. Fedorov
Samuel Lavoie-Marchildon
Karan Grewal
Phil Bachman
Adam Trischler
Yoshua Bengio
SSL
DRL
300
2,661
0
20 Aug 2018
Deep Convolutional Networks as shallow Gaussian Processes
Deep Convolutional Networks as shallow Gaussian Processes
Adrià Garriga-Alonso
C. Rasmussen
Laurence Aitchison
BDL
UQCV
93
270
0
16 Aug 2018
Representation Learning with Contrastive Predictive Coding
Representation Learning with Contrastive Predictive Coding
Aaron van den Oord
Yazhe Li
Oriol Vinyals
DRL
SSL
290
10,253
0
10 Jul 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Arthur Jacot
Franck Gabriel
Clément Hongler
244
3,191
0
20 Jun 2018
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables
  Signal Propagation in Recurrent Neural Networks
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
Minmin Chen
Jeffrey Pennington
S. Schoenholz
SyDa
AI4CE
52
116
0
14 Jun 2018
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train
  10,000-Layer Vanilla Convolutional Neural Networks
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Lechao Xiao
Yasaman Bahri
Jascha Narain Sohl-Dickstein
S. Schoenholz
Jeffrey Pennington
298
353
0
14 Jun 2018
Gaussian Process Behaviour in Wide Deep Neural Networks
Gaussian Process Behaviour in Wide Deep Neural Networks
A. G. Matthews
Mark Rowland
Jiri Hron
Richard Turner
Zoubin Ghahramani
BDL
141
557
0
30 Apr 2018
The Emergence of Spectral Universality in Deep Networks
The Emergence of Spectral Universality in Deep Networks
Jeffrey Pennington
S. Schoenholz
Surya Ganguli
56
173
0
27 Feb 2018
State Representation Learning for Control: An Overview
State Representation Learning for Control: An Overview
Timothée Lesort
Natalia Díaz Rodríguez
Jean-François Goudou
David Filliat
OffRL
96
320
0
12 Feb 2018
Mean Field Residual Networks: On the Edge of Chaos
Mean Field Residual Networks: On the Edge of Chaos
Greg Yang
S. Schoenholz
59
191
0
24 Dec 2017
Resurrecting the sigmoid in deep learning through dynamical isometry:
  theory and practice
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice
Jeffrey Pennington
S. Schoenholz
Surya Ganguli
ODL
43
252
0
13 Nov 2017
Gaussian Lower Bound for the Information Bottleneck Limit
Gaussian Lower Bound for the Information Bottleneck Limit
Amichai Painsky
Naftali Tishby
30
15
0
07 Nov 2017
Deep Neural Networks as Gaussian Processes
Deep Neural Networks as Gaussian Processes
Jaehoon Lee
Yasaman Bahri
Roman Novak
S. Schoenholz
Jeffrey Pennington
Jascha Narain Sohl-Dickstein
UQCV
BDL
113
1,091
0
01 Nov 2017
Representation Learning on Graphs: Methods and Applications
Representation Learning on Graphs: Methods and Applications
William L. Hamilton
Rex Ying
J. Leskovec
GNN
159
1,971
0
17 Sep 2017
Deep Variational Information Bottleneck
Deep Variational Information Bottleneck
Alexander A. Alemi
Ian S. Fischer
Joshua V. Dillon
Kevin Patrick Murphy
98
1,714
0
01 Dec 2016
Deep Information Propagation
Deep Information Propagation
S. Schoenholz
Justin Gilmer
Surya Ganguli
Jascha Narain Sohl-Dickstein
77
367
0
04 Nov 2016
Exponential expressivity in deep neural networks through transient chaos
Exponential expressivity in deep neural networks through transient chaos
Ben Poole
Subhaneil Lahiri
M. Raghu
Jascha Narain Sohl-Dickstein
Surya Ganguli
88
591
0
16 Jun 2016
Unsupervised Representation Learning with Deep Convolutional Generative
  Adversarial Networks
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford
Luke Metz
Soumith Chintala
GAN
OOD
243
13,989
0
19 Nov 2015
Exact solutions to the nonlinear dynamics of learning in deep linear
  neural networks
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M. Saxe
James L. McClelland
Surya Ganguli
ODL
162
1,844
0
20 Dec 2013
Representation Learning: A Review and New Perspectives
Representation Learning: A Review and New Perspectives
Yoshua Bengio
Aaron Courville
Pascal Vincent
OOD
SSL
233
12,422
0
24 Jun 2012
1