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2006.07721
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Beyond Random Matrix Theory for Deep Networks
13 June 2020
Diego Granziol
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Papers citing
"Beyond Random Matrix Theory for Deep Networks"
28 / 28 papers shown
Unifying Low Dimensional Observations in Deep Learning Through the Deep Linear Unconstrained Feature Model
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Jonathan P. Keating
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Boundary between noise and information applied to filtering neural network weight matrices
Physical Review E (Phys. Rev. E), 2022
Max Staats
M. Thamm
B. Rosenow
221
6
0
08 Jun 2022
Universal characteristics of deep neural network loss surfaces from random matrix theory
Nicholas P. Baskerville
J. Keating
F. Mezzadri
J. Najnudel
Diego Granziol
232
7
0
17 May 2022
Random matrix analysis of deep neural network weight matrices
Physical Review E (Phys. Rev. E), 2022
M. Thamm
Max Staats
B. Rosenow
305
22
0
28 Mar 2022
Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping
Journal of machine learning research (JMLR), 2021
Xuran Meng
Jianfeng Yao
319
12
0
26 Nov 2021
Analytic Insights into Structure and Rank of Neural Network Hessian Maps
Neural Information Processing Systems (NeurIPS), 2021
Sidak Pal Singh
Gregor Bachmann
Thomas Hofmann
FAtt
295
48
0
30 Jun 2021
Hessian Eigenspectra of More Realistic Nonlinear Models
Neural Information Processing Systems (NeurIPS), 2021
Zhenyu Liao
Michael W. Mahoney
418
40
0
02 Mar 2021
Appearance of Random Matrix Theory in Deep Learning
Nicholas P. Baskerville
Diego Granziol
J. Keating
464
13
0
12 Feb 2021
A spin-glass model for the loss surfaces of generative adversarial networks
Journal of statistical physics (J. Stat. Phys.), 2021
Nicholas P. Baskerville
J. Keating
F. Mezzadri
J. Najnudel
GAN
298
12
0
07 Jan 2021
The Loss Surfaces of Neural Networks with General Activation Functions
Journal of Statistical Mechanics: Theory and Experiment (JSTAT), 2020
Nicholas P. Baskerville
J. Keating
F. Mezzadri
J. Najnudel
ODL
AI4CE
483
28
0
08 Apr 2020
Average-case Acceleration Through Spectral Density Estimation
Fabian Pedregosa
Damien Scieur
445
13
0
12 Feb 2020
Deep Curvature Suite
Diego Granziol
Xingchen Wan
T. Garipov
3DV
240
12
0
20 Dec 2019
Limitations of the Empirical Fisher Approximation for Natural Gradient Descent
Neural Information Processing Systems (NeurIPS), 2019
Frederik Kunstner
Lukas Balles
Philipp Hennig
531
256
0
29 May 2019
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
Behrooz Ghorbani
Shankar Krishnan
Ying Xiao
ODL
495
398
0
29 Jan 2019
Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians
Vardan Papyan
205
91
0
24 Jan 2019
Averaging Weights Leads to Wider Optima and Better Generalization
Conference on Uncertainty in Artificial Intelligence (UAI), 2018
Pavel Izmailov
Dmitrii Podoprikhin
T. Garipov
Dmitry Vetrov
A. Wilson
FedML
MoMe
735
1,947
0
14 Mar 2018
Essentially No Barriers in Neural Network Energy Landscape
International Conference on Machine Learning (ICML), 2018
Felix Dräxler
K. Veschgini
M. Salmhofer
Fred Hamprecht
MoMe
690
510
0
02 Mar 2018
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Neural Information Processing Systems (NeurIPS), 2018
T. Garipov
Pavel Izmailov
Dmitrii Podoprikhin
Dmitry Vetrov
A. Wilson
UQCV
844
888
0
27 Feb 2018
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks
International Conference on Learning Representations (ICLR), 2017
Levent Sagun
Utku Evci
V. U. Güney
Yann N. Dauphin
Léon Bottou
470
462
0
14 Jun 2017
The loss surface of deep and wide neural networks
Quynh N. Nguyen
Matthias Hein
ODL
437
297
0
26 Apr 2017
Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond
Levent Sagun
Léon Bottou
Yann LeCun
UQCV
395
265
0
22 Nov 2016
Cleaning large correlation matrices: tools from random matrix theory
J. Bun
J. Bouchaud
M. Potters
299
291
0
25 Oct 2016
Deep Learning without Poor Local Minima
Kenji Kawaguchi
ODL
548
981
0
23 May 2016
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
4.1K
222,907
0
10 Dec 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
1.6K
46,205
0
11 Feb 2015
The Loss Surfaces of Multilayer Networks
International Conference on Artificial Intelligence and Statistics (AISTATS), 2014
A. Choromańska
Mikael Henaff
Michaël Mathieu
Gerard Ben Arous
Yann LeCun
ODL
910
1,281
0
30 Nov 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
International Conference on Learning Representations (ICLR), 2014
Karen Simonyan
Andrew Zisserman
FAtt
MDE
3.9K
110,044
0
04 Sep 2014
Distributions of Angles in Random Packing on Spheres
Journal of machine learning research (JMLR), 2013
Tony Cai
Jianqing Fan
Tiefeng Jiang
319
200
0
02 Jun 2013
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