Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
1810.05148
Cited By
v1
v2
v3
v4 (latest)
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes
11 October 2018
Roman Novak
Lechao Xiao
Jaehoon Lee
Yasaman Bahri
Greg Yang
Jiri Hron
Daniel A. Abolafia
Jeffrey Pennington
Jascha Narain Sohl-Dickstein
UQCV
BDL
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes"
48 / 48 papers shown
Title
Conditional Temporal Neural Processes with Covariance Loss
Boseon Yoo
Jiwoo Lee
Janghoon Ju
Seijun Chung
Soyeon Kim
Jaesik Choi
143
15
0
01 Apr 2025
Function-Space MCMC for Bayesian Wide Neural Networks
Lucia Pezzetti
Stefano Favaro
Stefano Peluchetti
BDL
428
0
0
26 Aug 2024
Bayesian RG Flow in Neural Network Field Theories
Jessica N. Howard
Marc S. Klinger
Anindita Maiti
A. G. Stapleton
105
2
0
27 May 2024
Random ReLU Neural Networks as Non-Gaussian Processes
Rahul Parhi
Pakshal Bohra
Ayoub El Biari
Mehrsa Pourya
Michael Unser
108
1
0
16 May 2024
Connecting NTK and NNGP: A Unified Theoretical Framework for Wide Neural Network Learning Dynamics
Yehonatan Avidan
Qianyi Li
H. Sompolinsky
108
8
0
08 Sep 2023
Neural Tangents: Fast and Easy Infinite Neural Networks in Python
Roman Novak
Lechao Xiao
Jiri Hron
Jaehoon Lee
Alexander A. Alemi
Jascha Narain Sohl-Dickstein
S. Schoenholz
86
229
0
05 Dec 2019
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
Greg Yang
128
201
0
28 Oct 2019
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation
Greg Yang
169
289
0
13 Feb 2019
On the effect of the activation function on the distribution of hidden nodes in a deep network
Philip M. Long
Hanie Sedghi
72
5
0
07 Jan 2019
A Gaussian Process perspective on Convolutional Neural Networks
Anastasia Borovykh
77
19
0
25 Oct 2018
Deep convolutional Gaussian processes
Kenneth Blomqvist
Samuel Kaski
Markus Heinonen
BDL
67
61
0
06 Oct 2018
Deep Convolutional Networks as shallow Gaussian Processes
Adrià Garriga-Alonso
C. Rasmussen
Laurence Aitchison
BDL
UQCV
109
271
0
16 Aug 2018
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
Sergey Bartunov
Adam Santoro
Blake A. Richards
Luke Marris
Geoffrey E. Hinton
Timothy Lillicrap
92
244
0
12 Jul 2018
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
57
117
0
14 Jun 2018
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
304
354
0
14 Jun 2018
Deep Gaussian Processes with Convolutional Kernels
Vinayak Kumar
Vaibhav Singh
P. K. Srijith
Andreas C. Damianou
BDL
GP
77
30
0
05 Jun 2018
Gaussian Process Behaviour in Wide Deep Neural Networks
A. G. Matthews
Mark Rowland
Jiri Hron
Richard Turner
Zoubin Ghahramani
BDL
155
561
0
30 Apr 2018
How to Start Training: The Effect of Initialization and Architecture
Boris Hanin
David Rolnick
82
255
0
05 Mar 2018
Sensitivity and Generalization in Neural Networks: an Empirical Study
Roman Novak
Yasaman Bahri
Daniel A. Abolafia
Jeffrey Pennington
Jascha Narain Sohl-Dickstein
AAML
95
441
0
23 Feb 2018
Mean Field Residual Networks: On the Edge of Chaos
Greg Yang
S. Schoenholz
71
194
0
24 Dec 2017
Deep Neural Networks as Gaussian Processes
Jaehoon Lee
Yasaman Bahri
Roman Novak
S. Schoenholz
Jeffrey Pennington
Jascha Narain Sohl-Dickstein
UQCV
BDL
135
1,099
0
01 Nov 2017
Convolutional Gaussian Processes
Mark van der Wilk
C. Rasmussen
J. Hensman
BDL
74
132
0
06 Sep 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao
Kashif Rasul
Roland Vollgraf
285
8,920
0
25 Aug 2017
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
John Bradshaw
A. G. Matthews
Zoubin Ghahramani
BDL
AAML
103
172
0
08 Jul 2017
Attention Is All You Need
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan Gomez
Lukasz Kaiser
Illia Polosukhin
3DV
786
132,363
0
12 Jun 2017
Understanding deep learning requires rethinking generalization
Chiyuan Zhang
Samy Bengio
Moritz Hardt
Benjamin Recht
Oriol Vinyals
HAI
348
4,635
0
10 Nov 2016
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review
T. Poggio
H. Mhaskar
Lorenzo Rosasco
Brando Miranda
Q. Liao
126
576
0
02 Nov 2016
Stochastic Variational Deep Kernel Learning
A. Wilson
Zhiting Hu
Ruslan Salakhutdinov
Eric Xing
BDL
117
268
0
01 Nov 2016
WaveNet: A Generative Model for Raw Audio
Aaron van den Oord
Sander Dieleman
Heiga Zen
Karen Simonyan
Oriol Vinyals
Alex Graves
Nal Kalchbrenner
A. Senior
Koray Kavukcuoglu
DiffM
406
7,414
0
12 Sep 2016
Why does deep and cheap learning work so well?
Henry W. Lin
Max Tegmark
David Rolnick
85
609
0
29 Aug 2016
Exponential expressivity in deep neural networks through transient chaos
Ben Poole
Subhaneil Lahiri
M. Raghu
Jascha Narain Sohl-Dickstein
Surya Ganguli
90
595
0
16 Jun 2016
An Analysis of Deep Neural Network Models for Practical Applications
A. Canziani
Adam Paszke
Eugenio Culurciello
90
1,167
0
24 May 2016
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi
Ashish Agarwal
P. Barham
E. Brevdo
Zhiwen Chen
...
Pete Warden
Martin Wattenberg
Martin Wicke
Yuan Yu
Xiaoqiang Zheng
285
11,150
0
14 Mar 2016
Group Equivariant Convolutional Networks
Taco S. Cohen
Max Welling
BDL
171
1,945
0
24 Feb 2016
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
Amit Daniely
Roy Frostig
Y. Singer
168
345
0
18 Feb 2016
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,426
0
10 Dec 2015
Deep Kernel Learning
A. Wilson
Zhiting Hu
Ruslan Salakhutdinov
Eric Xing
BDL
253
888
0
06 Nov 2015
Steps Toward Deep Kernel Methods from Infinite Neural Networks
Tamir Hazan
Tommi Jaakkola
82
83
0
20 Aug 2015
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
2.0K
150,312
0
22 Dec 2014
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
Behnam Neyshabur
Ryota Tomioka
Nathan Srebro
AI4CE
99
662
0
20 Dec 2014
Qualitatively characterizing neural network optimization problems
Ian Goodfellow
Oriol Vinyals
Andrew M. Saxe
ODL
112
523
0
19 Dec 2014
The Loss Surfaces of Multilayer Networks
A. Choromańska
Mikael Henaff
Michaël Mathieu
Gerard Ben Arous
Yann LeCun
ODL
263
1,200
0
30 Nov 2014
Do Convnets Learn Correspondence?
Jonathan Long
Ning Zhang
Trevor Darrell
86
311
0
04 Nov 2014
On the saddle point problem for non-convex optimization
Razvan Pascanu
Yann N. Dauphin
Surya Ganguli
Yoshua Bengio
ODL
68
105
0
19 May 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan
Andrea Vedaldi
Andrew Zisserman
FAtt
314
7,316
0
20 Dec 2013
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler
Rob Fergus
FAtt
SSL
595
15,902
0
12 Nov 2013
Deep Gaussian Processes
Andreas C. Damianou
Neil D. Lawrence
GP
BDL
146
1,184
0
02 Nov 2012
Invariant Scattering Convolution Networks
Joan Bruna
S. Mallat
131
1,279
0
05 Mar 2012
1