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Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications.
Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information
Emmanuel J Candès, Justin Romberg, and Terence Tao · 2006
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Compressed sensing
David L Donoho · 2006
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Optimal solutions for sparse principal component analysis
Alexandre d’Aspremont, Francis Bach, and Laurent El Ghaoui · 2008
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Predicting parameters in deep learning
Misha Denil, Babak Shakibi, Laurent Dinh, Marc’Aurelio Ranzato, and Nando De Freitas · 2013
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Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Dynamic network surgery for efficient dnns
Yiwen Guo, Anbang Yao, and Yurong Chen · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Parseval networks: Improving robustness to adversarial examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
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Deepcloak: Masking deep neural network models for robustness against adversarial samples
Ji Gao, Beilun Wang, Zeming Lin, Weilin Xu, and Yanjun Qi · 2017
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Formal guarantees on the robustness of a classifier against adversarial manipulation
Stochastic activation pruning for robust adversarial defense
Guneet S Dhillon, Kamyar Azizzadenesheli, Zachary C Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, and Anima Anandkumar · 2018
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Attacking binarized neural networks
Angus Galloway, Graham W Taylor, and Medhat Moussa · 2018
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Combating adversarial attacks using sparse representations
Soorya Gopalakrishnan, Zhinus Marzi, Upamanyu Madhow, and Ramtin Pedarsani · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Sparsity-based defense against adversarial attacks on linear classifiers
Zhinus Marzi, Soorya Gopalakrishnan, Upamanyu Madhow, and Ramtin Pedarsani · 2018
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Ensemble adversarial training: Attacks and defenses
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Matthias Hein and Maksym Andriushchenko · 2017
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Safetynet: Detecting and rejecting adversarial examples robustly
Jiajun Lu, Theerasit Issaranon, and David Forsyth · 2017
Cited alongside, same era.
Variational dropout sparsifies deep neural networks
Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2017
Cited alongside, same era.
Structured bayesian pruning via log-normal multiplicative noise
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, and Dmitry P Vetrov · 2017
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Faster cnns with direct sparse convolutions and guided pruning
Jongsoo Park, Sheng Li, Wei Wen, Ping Tak Peter Tang, Hai Li, Yiran Chen, and Pradeep Dubey · 2017
Cited alongside, same era.
Soft weight-sharing for neural network compression
Karen Ullrich, Edward Meeds, and Max Welling · 2017
Cited alongside, same era.
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Dan Boneh, and Patrick McDaniel · 2018
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Adversarial robustness of pruned neural networks
Luyu Wang, Gavin Weiguang Ding, Ruitong Huang, Yanshuai Cao, and Yik Chau Lui · 2018
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Evaluating the robustness of neural networks: An extreme value theory approach
Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, and Luca Daniel · 2018
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Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2018
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Defending DNN adversarial attacks with pruning and logits augmentation
Shaokai Ye, Siyue Wang, Xiao Wang, Bo Yuan, Wujie Wen, and Xue Lin · 2018
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