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Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks.
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Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
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Poisoning behavioral malware clustering
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Generative adversarial nets
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Imagenet classification with deep convolutional neural networks
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Machine learning: a probabilistic perspective
K. P. Murphy · 2012
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Evasion attacks against machine learning at test time
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli · 2013
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Large-scale malware classification using random projections and neural networks
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Pattern recognition systems under attack: Design issues and research challenges
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How transferable are features in deep neural networks?
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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S. Gu and L. Rigazio · 2015
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How paypal beats the bad guys with machine learning
E. Knorr · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
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Convolutional, long short-term memory, fully connected deep neural networks
T. N. Sainath, O. Vinyals, A. Senior, and H. Sak · 2015
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