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Neural networks with low-precision weights and activations offer compelling efficiency advantages over their full-precision equivalents.
The mnist database of handwritten digits
Yann LeCun and Corinna Cortes · 1998
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron C. Courville · 2013
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Evasion Attacks against Machine Learning at Test Time , pp. 387–402
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Explaining and Harnessing Adversarial Examples
Ian. J. Goodfellow, Jonathon. Shlens, and Christian. Szegedy · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2015
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami · 2015
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Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1
Matthieu Courbariaux and Yoshua Bengio · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Extending defensive distillation
Nicolas Papernot and Patrick D. McDaniel · 2017
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 2017
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Foolbox v0.8.0: A python toolbox to benchmark the robustness of machine learning models
Jonas Rauber, Wieland Brendel, and Matthias Bethge · 2017
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How to Train a Compact Binary Neural Network with High Accuracy?
Wei Tang, Gang Hua, and Liang Wang · 2017
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Adversarial machine learning at scale
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio
Cited in the paper.
Adversarial examples in the physical world
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio
Cited in the paper.
cleverhans v2.0.0: an adversarial machine learning library
Nicolas Papernot, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Fartash Faghri, Alexander Matyasko, Karen Hambardzumyan, Yi-Lin Juang, Alexey Kurakin, Ryan Sheatsley, Abhibhav Garg, and Yen-Chen Lin
Cited in the paper.
The space of transferable adversarial examples
Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
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