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Deep reinforcement learning has shown promising results in learning control policies for complex sequential decision-making tasks.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
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Explaining and harnessing adversarial examples
I. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
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Human-level control through deep reinforcement learning
V. e. a. Mnih · 2015
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Action-conditional video prediction using deep networks in atari games
J. Oh, X. Guo, H. Lee, R. L. Lewis, and S. Singh · 2015
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Trust region policy optimization
J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz · 2015
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Unsupervised learning of video representations using lstms
N. Srivastava, E. Mansimov, and R. Salakhutdinov · 2015
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End to end learning for self-driving cars
M. e. a. Bojarski · 2016
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Openai gym, 2016
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2016
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Unsupervised learning for physical interaction through video prediction
C. Finn, I. Goodfellow, and S. Levine · 2016
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Deep visual foresight for planning robot motion
C. Finn and S. Levine · 2016
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End-to-end training of deep visuomotor policies
S. Levine, C. Finn, T. Darrell, and P. Abbeel · 2016
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Adversarial examples detection in deep networks with convolutional filter statistics
X. Li and F. Li · 2016
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Deep multi-scale video prediction beyond mean square error
M. Mathieu, C. Couprie, and Y. LeCun · 2016
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Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, and M. Mirza · 2016
Cited alongside, same era.
Deepfool: A simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami · 2016
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
Cited alongside, same era.
Terrain-adaptive locomotion skills using deep reinforcement learning
X. B. Peng, G. Berseth, and M. Van de Panne · 2016
Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
D. e. a. Silver · 2016
Adversarial attacks on neural network policies
S. Huang, N. Papernot, I. Goodfellow, Y. Duan, and P. Abbeel · 2017
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Adversarial examples for generative models
J. Kos, I. Fischer, and D. Song · 2017
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Delving into adversarial attacks on deep policies
J. Kos and D. Song · 2017
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Adversarial machine learning at scale
A. Kurakin, I. Goodfellow, and S. Bengio · 2017
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Detecting adversarial examples in deep networks with adaptive noise reduction
B. Liang, H. Li, M. Su, X. Li, W. Shi, and X. Wang · 2017
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Cited alongside, same era.
Probabilistic modeling of future frames from a single image
T. Xue, J. Wu, K. Bouman, and B. Freeman · 2016
Cited alongside, same era.
Vulnerability of deep reinforcement learning to policy induction attacks
V. Behzadan and A. Munir · 2017
Cited alongside, same era.
Dimensionality reduction as a defense against evasion attacks on machine learning classifiers
A. N. Bhagoji, D. Cullina, and P. Mittal · 2017
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
N. Carlini and D. A. Wagner · 2017
Cited alongside, same era.
Houdini: Fooling deep structured prediction models
M. Cisse, Y. Adi, N. Neverova, and J. Keshet · 2017
Cited alongside, same era.
Detecting adversarial samples from artifacts
R. Feinman, R. R. Curtin, S. Shintre, and A. B. Gardner · 2017
Cited alongside, same era.
Y.-C. Lin, Z.-W. Hong, Y.-H. Liao, M.-L. Shih, M.-Y. Liu, and M. Sun · 2017
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Delving into transferable adversarial examples and black-box attacks
Y. Liu, X. Chen, C. Liu, and D. Song · 2017
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Magnet: a two-pronged defense against adversarial examples
D. Meng and H. Chen · 2017
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On detecting adversarial perturbations
J. H. Metzen, T. Genewein, V. Fischer, and B. Bischoff · 2017
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Extending defensive distillation
N. Papernot and P. McDaniel · 2017
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Practical black-box attacks against deep learning systems using adversarial examples
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2017
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Decomposing motion and content for natural video sequence prediction
R. Villegas, J. Yang, S. Hong, X. Lin, and H. Lee · 2017
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Adversarial examples for semantic segmentation and object detection
C. Xie, J. Wang, Z. Zhang, Y. Zhou, L. Xie, and A. Yuille · 2017
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Feature squeezing: Mitigates and detects carlini/wagner adversarial examples
W. Xu, D. Evans, and Y. Qi · 2017
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