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What training reveals about neural network complexity

What training reveals about neural network complexity

8 June 2021
Andreas Loukas
Marinos Poiitis
Stefanie Jegelka
ArXivPDFHTML

Papers citing "What training reveals about neural network complexity"

5 / 5 papers shown
Title
When Deep Learning Meets Polyhedral Theory: A Survey
When Deep Learning Meets Polyhedral Theory: A Survey
Joey Huchette
Gonzalo Muñoz
Thiago Serra
Calvin Tsay
AI4CE
91
32
0
29 Apr 2023
Dropout: Explicit Forms and Capacity Control
Dropout: Explicit Forms and Capacity Control
R. Arora
Peter L. Bartlett
Poorya Mianjy
Nathan Srebro
52
37
0
06 Mar 2020
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
273
2,888
0
15 Sep 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
261
9,134
0
06 Jun 2015
Improving neural networks by preventing co-adaptation of feature
  detectors
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
251
7,633
0
03 Jul 2012
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