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Deep learning has become the state of the art approach in many machine learning problems such as classification.
Neural network ensembles
Hansen, Lars Kai and Salamon, Peter · 1990
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Back propagation is sensitive to initial conditions
Kolen, John F and Pollack, Jordan B · 1991
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Bagging predictors
Breiman, Leo · 1996
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Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
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Ensemble methods in machine learning
Dietterich, Thomas G et al · 2000
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Traffic sign recognition with multi-scale convolutional networks
Sermanet, Pierre and LeCun, Yann · 2011
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Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
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Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
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Explaining and harnessing adversarial examples
Goodfellow, Ian J, Shlens, Jonathon, and Szegedy, Christian · 2014
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The limitations of deep learning in adversarial settings
Papernot, Nicolas, McDaniel, Patrick, Jha, Somesh, Fredrikson, Matt, Celik, Z Berkay, and Swami, Ananthram · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, Nicolas, McDaniel, Patrick, Wu, Xi, Jha, Somesh, and Swami, Ananthram · 2016
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Robustness to adversarial examples through an ensemble of specialists
Abbasi, Mahdieh and Gagné, Christian · 2017
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Towards evaluating the robustness of neural networks
Carlini, Nicholas and Wagner, David · 2017
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Detecting adversarial samples from artifacts
Feinman, Reuben, Curtin, Ryan R, Shintre, Saurabh, and Gardner, Andrew B · 2017
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Graham, Benjamin · 2014
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Towards deep neural network architectures robust to adversarial examples
Gu, Shixiang and Rigazio, Luca · 2014
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Deep learning
LeCun, Yann, Bengio, Yoshua, and Hinton, Geoffrey · 2015
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Adversarial examples in the physical world
Kurakin, Alexey, Goodfellow, Ian, and Bengio, Samy · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, Seyed-Mohsen, Fawzi, Alhussein, and Frossard, Pascal · 2016
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cleverhans v1. 0.0: an adversarial machine learning library
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Practical black-box attacks against deep learning systems using adversarial examples
Papernot, Nicolas, McDaniel, Patrick, Goodfellow, Ian, Jha, Somesh, Celik, Z Berkay, and Swami, Ananthram
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He, Warren, Wei, James, Chen, Xinyun, Carlini, Nicholas, and Song, Dawn · 2017
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On detecting adversarial perturbations
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