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An empirical analysis of dropout in piecewise linear networks

An empirical analysis of dropout in piecewise linear networks

21 December 2013
David Warde-Farley
Ian Goodfellow
Aaron Courville
Yoshua Bengio
ArXivPDFHTML

Papers citing "An empirical analysis of dropout in piecewise linear networks"

16 / 16 papers shown
Title
LoRA Dropout as a Sparsity Regularizer for Overfitting Control
LoRA Dropout as a Sparsity Regularizer for Overfitting Control
Yang Lin
Xinyu Ma
Xu Chu
Yujie Jin
Zhibang Yang
Yasha Wang
Hong-yan Mei
49
19
0
15 Apr 2024
Few-shot Image Generation via Masked Discrimination
Few-shot Image Generation via Masked Discrimination
Jin Zhu
Huimin Ma
Jiansheng Chen
Jian Yuan
27
12
0
27 Oct 2022
Information Geometry of Dropout Training
Information Geometry of Dropout Training
Masanari Kimura
H. Hino
9
2
0
22 Jun 2022
Quaternion Factorization Machines: A Lightweight Solution to Intricate
  Feature Interaction Modelling
Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling
Tong Chen
Hongzhi Yin
Xiangliang Zhang
Zi Huang
Yang Wang
Meng Wang
22
12
0
05 Apr 2021
Advances in Electron Microscopy with Deep Learning
Advances in Electron Microscopy with Deep Learning
Jeffrey M. Ede
29
2
0
04 Jan 2021
Prediction intervals for Deep Neural Networks
Prediction intervals for Deep Neural Networks
Tullio Mancini
Hector F. Calvo-Pardo
Jose Olmo
UQCV
OOD
20
4
0
08 Oct 2020
Review: Deep Learning in Electron Microscopy
Review: Deep Learning in Electron Microscopy
Jeffrey M. Ede
29
79
0
17 Sep 2020
BatchEnsemble: An Alternative Approach to Efficient Ensemble and
  Lifelong Learning
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning
Yeming Wen
Dustin Tran
Jimmy Ba
OOD
FedML
UQCV
19
482
0
17 Feb 2020
Survey of Dropout Methods for Deep Neural Networks
Survey of Dropout Methods for Deep Neural Networks
Alex Labach
Hojjat Salehinejad
S. Valaee
15
149
0
25 Apr 2019
Deep Representation with ReLU Neural Networks
Deep Representation with ReLU Neural Networks
Andreas Heinecke
W. Hwang
18
0
0
29 Mar 2019
Reduction of Overfitting in Diabetes Prediction Using Deep Learning
  Neural Network
Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network
Akm Ashiquzzaman
A. Tushar
Md. Rashedul Islam
Jong-Myon Kim
BDL
16
87
0
26 Jul 2017
Towards Dropout Training for Convolutional Neural Networks
Towards Dropout Training for Convolutional Neural Networks
Haibing Wu
Xiaodong Gu
19
298
0
01 Dec 2015
A Scale Mixture Perspective of Multiplicative Noise in Neural Networks
A Scale Mixture Perspective of Multiplicative Noise in Neural Networks
Eric T. Nalisnick
Anima Anandkumar
Padhraic Smyth
27
19
0
10 Jun 2015
Rectified Factor Networks
Rectified Factor Networks
Djork-Arné Clevert
Andreas Mayr
Thomas Unterthiner
Sepp Hochreiter
20
16
0
23 Feb 2015
Learning with Pseudo-Ensembles
Learning with Pseudo-Ensembles
Philip Bachman
O. Alsharif
Doina Precup
25
593
0
16 Dec 2014
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
266
7,634
0
03 Jul 2012
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