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DropNeuron: Simplifying the Structure of Deep Neural Networks

DropNeuron: Simplifying the Structure of Deep Neural Networks

23 June 2016
W. Pan
Hao Dong
Yike Guo
ArXivPDFHTML

Papers citing "DropNeuron: Simplifying the Structure of Deep Neural Networks"

7 / 7 papers shown
Title
On GNN explanability with activation rules
On GNN explanability with activation rules
Luca Veyrin-Forrer
Ataollah Kamal
Stefan Duffner
Marc Plantevit
C. Robardet
AI4CE
21
2
0
17 Jun 2024
What to Prune and What Not to Prune at Initialization
What to Prune and What Not to Prune at Initialization
Maha Haroon
8
0
0
06 Sep 2022
Structured Pruning of Neural Networks with Budget-Aware Regularization
Structured Pruning of Neural Networks with Budget-Aware Regularization
Carl Lemaire
Andrew Achkar
Pierre-Marc Jodoin
16
92
0
23 Nov 2018
MPDCompress - Matrix Permutation Decomposition Algorithm for Deep Neural
  Network Compression
MPDCompress - Matrix Permutation Decomposition Algorithm for Deep Neural Network Compression
Lazar Supic
R. Naous
Ranko Sredojevic
Aleksandra Faust
Vladimir M. Stojanović
17
4
0
30 May 2018
Compressing Neural Networks using the Variational Information Bottleneck
Compressing Neural Networks using the Variational Information Bottleneck
Bin Dai
Chen Zhu
David Wipf
MLT
20
178
0
28 Feb 2018
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
252
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
245
7,633
0
03 Jul 2012
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