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Deep learning frameworks leverage GPUs to perform massively-parallel computations over batches of many training examples efficiently.
“Dueling Network Architectures for Deep Reinforcement Learning”
Ziyu Wang et al · 2003
Earlier work this paper cites.
“The algorithmic foundations of differential privacy”
Cynthia Dwork and Aaron Roth · 2014
Earlier work this paper cites.
“Efficient per-example gradient computations”
Ian Goodfellow · 2015
Earlier work this paper cites.
“Variance reduction in sgd by distributed importance sampling”
Guillaume Alain et al · 2015
Earlier work this paper cites.
“Deep learning with differential privacy”
Martin Abadi et al · 2016
Cited alongside, same era.
“Membership inference attacks against machine learning models”
Reza Shokri, Marco Stronati, Congzheng Song and Vitaly Shmatikov · 2017
Cited alongside, same era.
“The secret sharer: Measuring unintended neural network memorization & extracting secrets”
Nicholas Carlini et al · 2018
Cited alongside, same era.
“Privacy risk in machine learning: Analyzing the connection to overfitting”
Samuel Yeom, Irene Giacomelli, Matt Fredrikson and Somesh Jha · 2018
Later among the works it cites.
“Protection against reconstruction and its applications in private federated learning”
Abhishek Bhowmick et al · 2018
Later among the works it cites.
“Exploiting unintended feature leakage in collaborative learning”
Luca Melis, Congzheng Song, Emiliano De and Vitaly Shmatikov · 2019
Closest in time.
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