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Dropping Activation Outputs with Localized First-layer Deep Network for
  Enhancing User Privacy and Data Security

Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security

20 November 2017
Hao Dong
Chao Wu
Zhen Wei
Yike Guo
ArXivPDFHTML

Papers citing "Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security"

4 / 4 papers shown
Title
MixNN: A design for protecting deep learning models
MixNN: A design for protecting deep learning models
Chao Liu
Hao Chen
Yusen Wu
Rui Jin
10
0
0
28 Mar 2022
Demystifying Swarm Learning: A New Paradigm of Blockchain-based
  Decentralized Federated Learning
Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning
Jialiang Han
Y. Ma
Yudong Han
33
14
0
14 Jan 2022
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
247
9,109
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
243
7,620
0
03 Jul 2012
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