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We present a probabilistic framework for studying adversarial attacks on discrete data.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Yoon Kim · 2014
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Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Understanding neural networks through representation erasure
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2016
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Crafting adversarial input sequences for recurrent neural networks
Nicolas Papernot, Patrick McDaniel, Ananthram Swami, and Richard Harang · 2016
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Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky · 2015
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Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang · 2017
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Deep text classification can be fooled
Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, and Wenchang Shi · 2017
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Towards crafting text adversarial samples
Suranjana Samanta and Sameep Mehta · 2017
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Black-box generation of adversarial text sequences to evade deep learning classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa, and Yanjun Qi · 2018
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