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Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains.
Learning representations by back-propagating errors
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Lua—an extensible extension language
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Gradient-based learning applied to document recognition
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Privacy-preserving data mining
R. Agrawal and R. Srikant · 2000
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Privacy preserving data mining
Y. Lindell and B. Pinkas · 2000
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k k -anonymity: A model for protecting privacy
L. Sweeney · 2002
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Introductory Lectures on Convex Optimization. A Basic Course
Y. Nesterov · 2004
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On k k -anonymity and the curse of dimensionality
C. C. Aggarwal · 2005
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Our data, ourselves: Privacy via distributed noise generation
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor · 2006
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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The cost of privacy: Destruction of data-mining utility in anonymized data publishing
J. Brickell and V. Shmatikov · 2008
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Differential privacy and robust statistics
C. Dwork and J. Lei · 2009
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What is the best multi-stage architecture for object recognition?
K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun · 2009
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Differentially private recommender systems: Building privacy into the Netflix Prize contenders
F. McSherry and I. Mironov · 2009
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Privacy integrated queries: An extensible platform for privacy-preserving data analysis
F. D. McSherry · 2009
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Boosting and differential privacy
C. Dwork, G. N. Rothblum, and S. Vadhan · 2010
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Differentially private combinatorial optimization
A. Gupta, K. Ligett, F. McSherry, A. Roth, and K. Talwar · 2010
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Differentially private empirical risk minimization
K. Chaudhuri, C. Monteleoni, and A. D. Sarwate · 2011
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Torch7: A Matlab-like environment for machine learning
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Adaptive subgradient methods for online learning and stochastic optimization
J. Duchi, E. Hazan, and Y. Singer · 2011
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A firm foundation for private data analysis
C. Dwork · 2011
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What can we learn privately?
S. P. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith · 2011
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Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on Facebook
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015 · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
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Efficient per-example gradient computations
I. Goodfellow · 2015
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Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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The composition theorem for differential privacy
P. Kairouz, S. Oh, and P. Viswanath · 2015
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Move evaluation in Go using deep convolutional neural networks
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On random weights and unsupervised feature learning
A. Saxe, P. W. Koh, Z. Chen, M. Bhand, B. Suresh, and A. Ng · 2011
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Private convex optimization for empirical risk minimization with applications to high-dimensional regression
D. Kifer, A. D. Smith, and A. Thakurta · 2012
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Accelerating stochastic gradient descent using predictive variance reduction
R. Johnson and T. Zhang · 2013
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Efficient estimation of word representations in vector space
T. Mikolov, K. Chen, G. Corrado, and J. Dean · 2013
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Stochastic gradient descent with differentially private updates
S. Song, K. Chaudhuri, and A. Sarwate · 2013
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C. J. Maddison, A. Huang, I. Sutskever, and D. Silver · 2015
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Privacy-preserving deep learning
R. Shokri and V. Shmatikov · 2015
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Going deeper with convolutions
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Grammar as a foreign language
O. Vinyals, L. Kaiser, T. Koo, S. Petrov, I. Sutskever, and G. E. Hinton · 2015
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Algorithmic stability for adaptive data analysis
R. Bassily, K. Nissim, A. Smith, T. Steinke, U. Stemmer, and J. Ullman · 2016
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A. Daniely, R. Frostig, and Y. Singer · 2016
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Concentrated differential privacy
C. Dwork and G. N. Rothblum · 2016
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Rényi differential privacy
I. Mironov · 2016
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Differential privacy preservation for deep auto-encoders: an application of human behavior prediction
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Mastering the game of Go with deep neural networks and tree search
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Large scale kernel learning using block coordinate descent
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Differentially private stochastic gradient descent for in-RDBMS analytics
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