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Beyond Random Matrix Theory for Deep Networks

Beyond Random Matrix Theory for Deep Networks

13 June 2020
Diego Granziol
ArXivPDFHTML

Papers citing "Beyond Random Matrix Theory for Deep Networks"

18 / 18 papers shown
Title
Average-case Acceleration Through Spectral Density Estimation
Average-case Acceleration Through Spectral Density Estimation
Fabian Pedregosa
Damien Scieur
18
12
0
12 Feb 2020
Deep Curvature Suite
Deep Curvature Suite
Diego Granziol
Xingchen Wan
T. Garipov
3DV
26
12
0
20 Dec 2019
Limitations of the Empirical Fisher Approximation for Natural Gradient
  Descent
Limitations of the Empirical Fisher Approximation for Natural Gradient Descent
Frederik Kunstner
Lukas Balles
Philipp Hennig
58
212
0
29 May 2019
An Investigation into Neural Net Optimization via Hessian Eigenvalue
  Density
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
Behrooz Ghorbani
Shankar Krishnan
Ying Xiao
ODL
42
320
0
29 Jan 2019
Measurements of Three-Level Hierarchical Structure in the Outliers in
  the Spectrum of Deepnet Hessians
Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians
Vardan Papyan
47
87
0
24 Jan 2019
Averaging Weights Leads to Wider Optima and Better Generalization
Averaging Weights Leads to Wider Optima and Better Generalization
Pavel Izmailov
Dmitrii Podoprikhin
T. Garipov
Dmitry Vetrov
A. Wilson
FedML
MoMe
93
1,643
0
14 Mar 2018
Essentially No Barriers in Neural Network Energy Landscape
Essentially No Barriers in Neural Network Energy Landscape
Felix Dräxler
K. Veschgini
M. Salmhofer
Fred Hamprecht
MoMe
97
430
0
02 Mar 2018
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
T. Garipov
Pavel Izmailov
Dmitrii Podoprikhin
Dmitry Vetrov
A. Wilson
UQCV
61
746
0
27 Feb 2018
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks
Levent Sagun
Utku Evci
V. U. Güney
Yann N. Dauphin
Léon Bottou
41
416
0
14 Jun 2017
The loss surface of deep and wide neural networks
The loss surface of deep and wide neural networks
Quynh N. Nguyen
Matthias Hein
ODL
89
284
0
26 Apr 2017
Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond
Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond
Levent Sagun
Léon Bottou
Yann LeCun
UQCV
71
233
0
22 Nov 2016
Cleaning large correlation matrices: tools from random matrix theory
Cleaning large correlation matrices: tools from random matrix theory
J. Bun
J. Bouchaud
M. Potters
60
263
0
25 Oct 2016
Deep Learning without Poor Local Minima
Deep Learning without Poor Local Minima
Kenji Kawaguchi
ODL
165
922
0
23 May 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
1.4K
192,638
0
10 Dec 2015
Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
328
43,154
0
11 Feb 2015
The Loss Surfaces of Multilayer Networks
The Loss Surfaces of Multilayer Networks
A. Choromańska
Mikael Henaff
Michaël Mathieu
Gerard Ben Arous
Yann LeCun
ODL
230
1,191
0
30 Nov 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAtt
MDE
943
99,991
0
04 Sep 2014
Distributions of Angles in Random Packing on Spheres
Distributions of Angles in Random Packing on Spheres
Tony Cai
Jianqing Fan
Tiefeng Jiang
102
183
0
02 Jun 2013
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