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Traditional and Heavy-Tailed Self Regularization in Neural Network
  Models

Traditional and Heavy-Tailed Self Regularization in Neural Network Models

24 January 2019
Charles H. Martin
Michael W. Mahoney
ArXiv (abs)PDFHTML

Papers citing "Traditional and Heavy-Tailed Self Regularization in Neural Network Models"

13 / 13 papers shown
Title
Convergence, Sticking and Escape: Stochastic Dynamics Near Critical Points in SGD
Convergence, Sticking and Escape: Stochastic Dynamics Near Critical Points in SGD
Dmitry Dudukalov
Artem Logachov
Vladimir Lotov
Timofei Prasolov
Evgeny Prokopenko
Anton Tarasenko
45
0
0
24 May 2025
An Investigation of the Weight Space to Monitor the Training Progress of
  Neural Networks
An Investigation of the Weight Space to Monitor the Training Progress of Neural Networks
Konstantin Schurholt
Damian Borth
81
3
0
18 Jun 2020
Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very
  Large Pre-Trained Deep Neural Networks
Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks
Charles H. Martin
Michael W. Mahoney
44
56
0
24 Jan 2019
Implicit Self-Regularization in Deep Neural Networks: Evidence from
  Random Matrix Theory and Implications for Learning
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning
Charles H. Martin
Michael W. Mahoney
AI4CE
109
201
0
02 Oct 2018
Regularization for Deep Learning: A Taxonomy
Regularization for Deep Learning: A Taxonomy
J. Kukačka
Vladimir Golkov
Daniel Cremers
82
336
0
29 Oct 2017
Rethinking generalization requires revisiting old ideas: statistical
  mechanics approaches and complex learning behavior
Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior
Charles H. Martin
Michael W. Mahoney
AI4CE
59
64
0
26 Oct 2017
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Priya Goyal
Piotr Dollár
Ross B. Girshick
P. Noordhuis
Lukasz Wesolowski
Aapo Kyrola
Andrew Tulloch
Yangqing Jia
Kaiming He
3DH
128
3,681
0
08 Jun 2017
Train longer, generalize better: closing the generalization gap in large
  batch training of neural networks
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Elad Hoffer
Itay Hubara
Daniel Soudry
ODL
178
799
0
24 May 2017
Understanding deep learning requires rethinking generalization
Understanding deep learning requires rethinking generalization
Chiyuan Zhang
Samy Bengio
Moritz Hardt
Benjamin Recht
Oriol Vinyals
HAI
342
4,629
0
10 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
72
264
0
25 Oct 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp
  Minima
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
N. Keskar
Dheevatsa Mudigere
J. Nocedal
M. Smelyanskiy
P. T. P. Tang
ODL
427
2,941
0
15 Sep 2016
The Loss Surfaces of Multilayer Networks
The Loss Surfaces of Multilayer Networks
A. Choromańska
Mikael Henaff
Michaël Mathieu
Gerard Ben Arous
Yann LeCun
ODL
261
1,198
0
30 Nov 2014
Limit Theory for the largest eigenvalues of sample covariance matrices
  with heavy-tails
Limit Theory for the largest eigenvalues of sample covariance matrices with heavy-tails
Richard A. Davis
Oliver Pfaffel
R. Stelzer
101
37
0
27 Aug 2011
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