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Exponential expressivity in deep neural networks through transient chaos

Exponential expressivity in deep neural networks through transient chaos

16 June 2016
Ben Poole
Subhaneil Lahiri
M. Raghu
Jascha Narain Sohl-Dickstein
Surya Ganguli
ArXivPDFHTML

Papers citing "Exponential expressivity in deep neural networks through transient chaos"

50 / 148 papers shown
Title
Bayesian neural network unit priors and generalized Weibull-tail
  property
Bayesian neural network unit priors and generalized Weibull-tail property
M. Vladimirova
Julyan Arbel
Stéphane Girard
BDL
54
9
0
06 Oct 2021
On the Impact of Stable Ranks in Deep Nets
On the Impact of Stable Ranks in Deep Nets
B. Georgiev
L. Franken
Mayukh Mukherjee
Georgios Arvanitidis
21
3
0
05 Oct 2021
Deep Neural Networks and Tabular Data: A Survey
Deep Neural Networks and Tabular Data: A Survey
V. Borisov
Tobias Leemann
Kathrin Seßler
Johannes Haug
Martin Pawelczyk
Gjergji Kasneci
LMTD
47
648
0
05 Oct 2021
Deformed semicircle law and concentration of nonlinear random matrices
  for ultra-wide neural networks
Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks
Zhichao Wang
Yizhe Zhu
35
18
0
20 Sep 2021
Neural Operator: Learning Maps Between Function Spaces
Neural Operator: Learning Maps Between Function Spaces
Nikola B. Kovachki
Zong-Yi Li
Burigede Liu
Kamyar Azizzadenesheli
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
AI4CE
52
440
0
19 Aug 2021
Convergence of Deep ReLU Networks
Convergence of Deep ReLU Networks
Yuesheng Xu
Haizhang Zhang
37
27
0
27 Jul 2021
Towards quantifying information flows: relative entropy in deep neural
  networks and the renormalization group
Towards quantifying information flows: relative entropy in deep neural networks and the renormalization group
J. Erdmenger
Kevin T. Grosvenor
R. Jefferson
54
17
0
14 Jul 2021
Precise characterization of the prior predictive distribution of deep
  ReLU networks
Precise characterization of the prior predictive distribution of deep ReLU networks
Lorenzo Noci
Gregor Bachmann
Kevin Roth
Sebastian Nowozin
Thomas Hofmann
BDL
UQCV
29
32
0
11 Jun 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian
  Process Perspective
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
Geoff Pleiss
John P. Cunningham
28
24
0
11 Jun 2021
Activation function design for deep networks: linearity and effective
  initialisation
Activation function design for deep networks: linearity and effective initialisation
Michael Murray
V. Abrol
Jared Tanner
ODL
LLMSV
29
18
0
17 May 2021
Analyzing Monotonic Linear Interpolation in Neural Network Loss
  Landscapes
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
James Lucas
Juhan Bae
Michael Ruogu Zhang
Stanislav Fort
R. Zemel
Roger C. Grosse
MoMe
164
28
0
22 Apr 2021
Deep ReLU Networks Preserve Expected Length
Deep ReLU Networks Preserve Expected Length
Boris Hanin
Ryan Jeong
David Rolnick
29
14
0
21 Feb 2021
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for
  Deep ReLU Networks
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks
Quynh N. Nguyen
Marco Mondelli
Guido Montúfar
25
81
0
21 Dec 2020
On 1/n neural representation and robustness
On 1/n neural representation and robustness
Josue Nassar
Piotr A. Sokól
SueYeon Chung
K. Harris
Il Memming Park
AAML
OOD
24
23
0
08 Dec 2020
On Random Matrices Arising in Deep Neural Networks: General I.I.D. Case
On Random Matrices Arising in Deep Neural Networks: General I.I.D. Case
L. Pastur
V. Slavin
CML
24
12
0
20 Nov 2020
Chaos and Complexity from Quantum Neural Network: A study with Diffusion
  Metric in Machine Learning
Chaos and Complexity from Quantum Neural Network: A study with Diffusion Metric in Machine Learning
S. Choudhury
Ankan Dutta
Debisree Ray
22
21
0
16 Nov 2020
Stable ResNet
Stable ResNet
Soufiane Hayou
Eugenio Clerico
Bo He
George Deligiannidis
Arnaud Doucet
Judith Rousseau
ODL
SSeg
46
51
0
24 Oct 2020
Tensor Programs III: Neural Matrix Laws
Tensor Programs III: Neural Matrix Laws
Greg Yang
14
44
0
22 Sep 2020
Review: Deep Learning in Electron Microscopy
Review: Deep Learning in Electron Microscopy
Jeffrey M. Ede
36
79
0
17 Sep 2020
Explicit Regularisation in Gaussian Noise Injections
Explicit Regularisation in Gaussian Noise Injections
A. Camuto
M. Willetts
Umut Simsekli
Stephen J. Roberts
Chris Holmes
25
55
0
14 Jul 2020
Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets
Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets
Kunal Sharma
M. Cerezo
Zoë Holmes
L. Cincio
A. Sornborger
Patrick J. Coles
24
48
0
09 Jul 2020
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural
  Network Initialization?
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?
Yaniv Blumenfeld
D. Gilboa
Daniel Soudry
ODL
30
13
0
02 Jul 2020
Deep Isometric Learning for Visual Recognition
Deep Isometric Learning for Visual Recognition
Haozhi Qi
Chong You
Xueliang Wang
Yi Ma
Jitendra Malik
VLM
35
54
0
30 Jun 2020
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders
Yibo Jiang
Cengiz Pehlevan
19
13
0
30 Jun 2020
Tensor Programs II: Neural Tangent Kernel for Any Architecture
Tensor Programs II: Neural Tangent Kernel for Any Architecture
Greg Yang
58
135
0
25 Jun 2020
Deep Residual Mixture Models
Deep Residual Mixture Models
Perttu Hämäläinen
Martin Trapp
Tuure Saloheimo
Arno Solin
36
8
0
22 Jun 2020
Neural Anisotropy Directions
Neural Anisotropy Directions
Guillermo Ortiz-Jiménez
Apostolos Modas
Seyed-Mohsen Moosavi-Dezfooli
P. Frossard
34
16
0
17 Jun 2020
Is deeper better? It depends on locality of relevant features
Is deeper better? It depends on locality of relevant features
Takashi Mori
Masahito Ueda
OOD
25
4
0
26 May 2020
Spectra of the Conjugate Kernel and Neural Tangent Kernel for
  linear-width neural networks
Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks
Z. Fan
Zhichao Wang
44
71
0
25 May 2020
Learning the gravitational force law and other analytic functions
Learning the gravitational force law and other analytic functions
Atish Agarwala
Abhimanyu Das
Rina Panigrahy
Qiuyi Zhang
MLT
16
0
0
15 May 2020
ReZero is All You Need: Fast Convergence at Large Depth
ReZero is All You Need: Fast Convergence at Large Depth
Thomas C. Bachlechner
Bodhisattwa Prasad Majumder
H. H. Mao
G. Cottrell
Julian McAuley
AI4CE
21
276
0
10 Mar 2020
Untangling in Invariant Speech Recognition
Untangling in Invariant Speech Recognition
Cory Stephenson
J. Feather
Suchismita Padhy
Oguz H. Elibol
Hanlin Tang
Josh H. McDermott
SueYeon Chung
SSL
30
30
0
03 Mar 2020
Any Target Function Exists in a Neighborhood of Any Sufficiently Wide
  Random Network: A Geometrical Perspective
Any Target Function Exists in a Neighborhood of Any Sufficiently Wide Random Network: A Geometrical Perspective
S. Amari
27
12
0
20 Jan 2020
On Random Matrices Arising in Deep Neural Networks. Gaussian Case
On Random Matrices Arising in Deep Neural Networks. Gaussian Case
L. Pastur
21
23
0
17 Jan 2020
Deep Learning-Based Solvability of Underdetermined Inverse Problems in
  Medical Imaging
Deep Learning-Based Solvability of Underdetermined Inverse Problems in Medical Imaging
Chang Min Hyun
Seong Hyeon Baek
M. Lee
S. Lee
J.K. Seo
27
39
0
06 Jan 2020
Empirical Studies on the Properties of Linear Regions in Deep Neural
  Networks
Empirical Studies on the Properties of Linear Regions in Deep Neural Networks
Xiao Zhang
Dongrui Wu
21
38
0
04 Jan 2020
A Comprehensive and Modularized Statistical Framework for Gradient Norm
  Equality in Deep Neural Networks
A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks
Zhaodong Chen
Lei Deng
Bangyan Wang
Guoqi Li
Yuan Xie
35
28
0
01 Jan 2020
Towards Efficient Training for Neural Network Quantization
Towards Efficient Training for Neural Network Quantization
Qing Jin
Linjie Yang
Zhenyu A. Liao
MQ
19
42
0
21 Dec 2019
Mean field theory for deep dropout networks: digging up gradient
  backpropagation deeply
Mean field theory for deep dropout networks: digging up gradient backpropagation deeply
Wei Huang
R. Xu
Weitao Du
Yutian Zeng
Yunce Zhao
25
6
0
19 Dec 2019
Optimization for deep learning: theory and algorithms
Optimization for deep learning: theory and algorithms
Ruoyu Sun
ODL
27
168
0
19 Dec 2019
Neural Tangents: Fast and Easy Infinite Neural Networks in Python
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
38
225
0
05 Dec 2019
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
33
193
0
28 Oct 2019
From complex to simple : hierarchical free-energy landscape renormalized
  in deep neural networks
From complex to simple : hierarchical free-energy landscape renormalized in deep neural networks
H. Yoshino
22
6
0
22 Oct 2019
On the expected behaviour of noise regularised deep neural networks as
  Gaussian processes
On the expected behaviour of noise regularised deep neural networks as Gaussian processes
Arnu Pretorius
Herman Kamper
Steve Kroon
27
9
0
12 Oct 2019
Neural networks are a priori biased towards Boolean functions with low
  entropy
Neural networks are a priori biased towards Boolean functions with low entropy
Chris Mingard
Joar Skalse
Guillermo Valle Pérez
David Martínez-Rubio
Vladimir Mikulik
A. Louis
FAtt
AI4CE
24
37
0
25 Sep 2019
Optimal Machine Intelligence at the Edge of Chaos
Optimal Machine Intelligence at the Edge of Chaos
Ling Feng
Lin Zhang
C. Lai
33
8
0
11 Sep 2019
Optimal Function Approximation with Relu Neural Networks
Optimal Function Approximation with Relu Neural Networks
Bo Liu
Yi Liang
25
33
0
09 Sep 2019
The Normalization Method for Alleviating Pathological Sharpness in Wide
  Neural Networks
The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks
Ryo Karakida
S. Akaho
S. Amari
27
40
0
07 Jun 2019
Infinitely deep neural networks as diffusion processes
Infinitely deep neural networks as diffusion processes
Stefano Peluchetti
Stefano Favaro
ODL
14
31
0
27 May 2019
Interpretable deep Gaussian processes with moments
Interpretable deep Gaussian processes with moments
Chi-Ken Lu
Scott Cheng-Hsin Yang
Xiaoran Hao
Patrick Shafto
18
19
0
27 May 2019
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