Fetching the paper…
Reading the bibliography…
This paper proposes a class of well-conditioned neural networks in which a unit amount of change in the inputs causes at most a unit amount of change in the outputs or any of the internal layers.
Improving generalization performance using double backpropagation
Harris Drucker and Yann Le Cun · 1992
Earlier work this paper cites.
A theoretical analysis of feature pooling in visual recognition
Y-Lan Boureau, Jean Ponce, and Yann LeCun · 2010
Earlier work this paper cites.
Robustness and generalization
Huan Xu and Shie Mannor · 2012
Earlier work this paper cites.
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Understanding and improving convolutional neural networks via concatenated rectified linear units
Wenling Shang, Kihyuk Sohn, Diogo Almeida, and Honglak Lee · 2016
Earlier work this paper cites.
Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus J Telgarsky · 2017
Earlier work this paper cites.
Provably minimally-distorted adversarial examples
Nicholas Carlini, Guy Katz, Clark Barrett, and David L Dill · 2017
Earlier work this paper cites.
Show-and-fool: Crafting adversarial examples for neural image captioning
Hongge Chen, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, and Cho-Jui Hsieh · 2017
Earlier work this paper cites.
Parseval networks: Improving robustness to adversarial examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
Earlier work this paper cites.
Hotflip: White-box adversarial examples for nlp
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou · 2017
Cited alongside, same era.
Stable architectures for deep neural networks
Eldad Haber and Lars Ruthotto · 2017
Cited alongside, same era.
Formal guarantees on the robustness of a classifier against adversarial manipulation
Matthias Hein and Maksym Andriushchenko · 2017
Cited alongside, same era.
Generalization in deep learning
Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio · 2017
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
J. Zico Kolter and Eric Wong · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Later among the works it cites.
Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal Mian · 2018
Closest in time.
Did you hear that? Adversarial examples against automatic speech recognition
Moustafa Alzantot, Bharathan Balaji, and Mani Srivastava · 2018
Closest in time.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Closest in time.
Audio adversarial examples: Targeted attacks on speech-to-text
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Cited alongside, same era.
Magnet: a two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
Cited alongside, same era.
Andrew Slavin Ross and Finale Doshi-Velez · 2017
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Dan Boneh, and Patrick McDaniel · 2017
Cited alongside, same era.
Spectral norm regularization for improving the generalizability of deep learning
Yuichi Yoshida and Takeru Miyato · 2017
Cited alongside, same era.
Efficient defenses against adversarial attacks
Valentina Zantedeschi, Maria-Irina Nicolae, and Ambrish Rawat · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner
Cited in the paper.
Nicholas Carlini and David Wagner · 2018
Closest in time.
Black-box generation of adversarial text sequences to evade deep learning classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa, and Yanjun Qi · 2018
Closest in time.
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S Schoenholz, Maithra Raghu, Martin Wattenberg, and Ian Goodfellow · 2018
Closest in time.
Fooling end-to-end speaker verification by adversarial examples
Felix Kreuk, Yossi Adi, Moustapha Cisse, and Joseph Keshet · 2018
Closest in time.
Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
Closest in time.
Scaling provable adversarial defenses
Eric Wong, Frank Schmidt, Jan Hendrik Metzen, and J Zico Kolter · 2018
Closest in time.
Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
Closest in time.