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Recent work has shown deep neural networks (DNNs) to be highly susceptible to well-designed, small perturbations at the input layer, or so-called adversarial examples.
Distributed hierarchical processing in the primate cerebral cortex
Felleman, Daniel J and Van Essen, David C · 1991
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Recurrent excitation in neocortical circuits
Douglas, Rodney J, Koch, Christof, Mahowald, Misha, Martin, KA, and Suarez, Humbert H · 1995
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The mnist database of handwritten digits, 1998
LeCun, Yann and Cortes, Corinna · 1998
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Learning deep architectures for ai
Bengio, Yoshua · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, Alex and Hinton, Geoffrey · 2009
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Cudamat: a cuda-based matrix class for python
Mnih, Volodymyr · 2009
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Transforming auto-encoders
Hinton, Geoffrey E, Krizhevsky, Alex, and Wang, Sida D · 2011
Cited alongside, same era.
What regularized auto-encoders learn from the data generating distribution
Alain, Guillaume and Bengio, Yoshua · 2012
Cited alongside, same era.
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, George E, Yu, Dong, Deng, Li, and Acero, Alex · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
Cited alongside, same era.
Learning with recursive perceptual representations
Vinyals, Oriol, Jia, Yangqing, Deng, Li, and Darrell, Trevor · 2012
Cited alongside, same era.
Deepface: Closing the gap to human-level performance in face verification
Deep learning using support vector machines
Tang, Yichuan · 2013
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Panda: Pose aligned networks for deep attribute modeling
Zhang, Ning, Paluri, Manohar, Ranzato, Marc’Aurelio, Darrell, Trevor, and Bourdev, Lubomir · 2013
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Marginalized denoising auto-encoders for nonlinear representations
Chen, Minmin, Weinberger, Kilian, Sha, Fei, and Bengio, Yoshua · 2014
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Avoiding pathologies in very deep networks
Duvenand, David, Rippel, Oren, Adams, Ryan P., and Ghahramani, Zoubin · 2014
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Recurrent models of visual attention
Mnih, Volodymyr, Heess, Nicolas, Graves, Alex, and Kavukcuoglu, Koray · 2014
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Taigman, Yaniv, Yang, Ming, Ranzato, Marc’Aurelio, and Wolf, Lior · 2013
Cited alongside, same era.
Higher order contractive auto-encoder
Rifai, Salah, Mesnil, Grégoire, Vincent, Pascal, Muller, Xavier, Bengio, Yoshua, Dauphin, Yann, and Glorot, Xavier
Cited in the paper.
Contractive auto-encoders: Explicit invariance during feature extraction
Rifai, Salah, Vincent, Pascal, Muller, Xavier, Glorot, Xavier, and Bengio, Yoshua
Cited in the paper.
Going deeper with convolutions
Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew
Cited in the paper.
Intriguing properties of neural networks
Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian, and Fergus, Rob
Cited in the paper.
Closest in time.
Deep networks with internal selective attention through feedback connections
Stollenga, Marijn, Masci, Jonathan, Gomez, Faustino, and Schmidhuber, Juergen · 2014
Closest in time.