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We ask whether neural networks can learn to use secret keys to protect information from other neural networks.
Probabilistic encryption
Shafi Goldwasser and Silvio Micali · 1984
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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Applied neuro-cryptography
Sébastien Dourlens · 1996
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Neural cryptography
Wolfgang Kinzel and Ido Kanter · 2002
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Analysis of neural cryptography
Alexander Klimov, Anton Mityagin, and Adi Shamir · 2002
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Neural synchronization and cryptography
Andreas Ruttor · 2007
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On the (im)possibility of obfuscating programs
Boaz Barak, Oded Goldreich, Russell Impagliazzo, Steven Rudich, Amit Sahai, Salil Vadhan, and Ke Yang · 2012
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Fully automated analysis of padding-based encryption in the computational model
Gilles Barthe, Juan Manuel Crespo, Benjamin Grégoire, César Kunz, Yassine Lakhnech, Benedikt Schmidt, and Santiago Zanella-Béguelin · 2013
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Characterization and computation of local nash equilibria in continuous games
Lillian J Ratliff, Samuel A Burden, and S Shankar Sastry · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2013
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Crypto-nets: Neural networks over encrypted data
Pengtao Xie, Misha Bilenko, Tom Finley, Ran Gilad-Bachrach, Kristin E. Lauter, and Michael Naehrig · 2014
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard S. Zemel · 2015
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Neural programmer: Inducing latent programs with gradient descent
Arvind Neelakantan, Quoc V. Le, and Ilya Sutskever · 2015
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InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin E. Lauter, Michael Naehrig, and John Wernsing · 2016
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Deep generative image models using a Laplacian pyramid of adversarial networks
Emily L. Denton, Soumith Chintala, Arthur Szlam, and Robert Fergus · 2015
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Censoring representations with an adversary
Harrison Edwards and Amos J. Storkey · 2015
Cited alongside, same era.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor S. Lempitsky · 2015
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Gregory S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian J. Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Józefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Gordon Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul A. Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda B. Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng
Cited in the paper.
TensorFlow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zhang
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Learning to communicate to solve riddles with deep distributed recurrent Q-networks
Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, and Shimon Whiteson
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Learning to communicate with deep multi-agent reinforcement learning
Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, and Shimon Whiteson
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f-GAN: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Improved techniques for training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Arthur Szlam, and Rob Fergus · 2016
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