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SHADE: Information Based Regularization for Deep Learning

SHADE: Information Based Regularization for Deep Learning

29 April 2018
Michael Blot
Thomas Robert
Nicolas Thome
Matthieu Cord
ArXivPDFHTML

Papers citing "SHADE: Information Based Regularization for Deep Learning"

4 / 4 papers shown
Title
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial
  Estimation
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation
Alexandre Ramé
Matthieu Cord
FedML
48
51
0
14 Jan 2021
AL2: Progressive Activation Loss for Learning General Representations in
  Classification Neural Networks
AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks
Majed El Helou
Frederike Dumbgen
Sabine Süsstrunk
CLL
AI4CE
30
2
0
07 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
281
2,889
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
285
9,138
0
06 Jun 2015
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