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Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications.
Statistical guarantees for the robustness of bayesian neural networks
Luca Cardelli, Marta Kwiatkowska, Luca Laurenti, Nicola Paoletti, Andrea Patane, and Matthew Wicker · 1903
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Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop · 2006
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Gaussian processes for machine learning , volume 2
Christopher KI Williams and Carl Edward Rasmussen · 2006
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Mcmc using hamiltonian dynamics
Radford M Neal et al · 2011
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Bayesian reasoning and machine learning
David Barber · 2012
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Bayesian learning for neural networks , volume 118
Radford M Neal · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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A novel graph-based fisher kernel method for semi-supervised learning
Alessandro Rozza, Mario Manzo, and Alfredo Petrosino · 2014
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Weight uncertainty in neural networks
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Towards evaluating the robustness of neural networks, 2016
Nicholas Carlini and David Wagner · 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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Tesla driver dies in first fatal crash while using autopilot mode
Danny Yadron and Dan Tynan · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
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Detecting adversarial samples from artifacts
Reuben Feinman, Ryan R Curtin, Saurabh Shintre, and Andrew B Gardner · 2017
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Dropout inference in bayesian neural networks with alpha-divergences
Yingzhen Li and Yarin Gal · 2017
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Towards deep learning models resistant to adversarial attacks, 2017
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Practical black-box attacks against machine learning
Yarin Gal and Lewis Smith · 2018
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Black-box adversarial attacks with limited queries and information
Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin · 2018
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On the geometry of adversarial examples
Marc Khoury and Dylan Hadfield-Menell · 2018
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Adv-bnn: Improved adversarial defense through robust bayesian neural network
Xuanqing Liu, Yao Li, Chongruo Wu, and Cho-Jui Hsieh · 2018
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A mean field view of the landscape of two-layer neural networks
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Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
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Adversarial phenomenon in the eyes of bayesian deep learning
Ambrish Rawat, Martin Wistuba, and Maria-Irina Nicolae · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Bayesian adversarial spheres: Bayesian inference and adversarial examples in a noiseless setting
Artur Bekasov and Iain Murray · 2018
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Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
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Gradient descent finds global minima of deep neural networks
Simon S Du, Jason D Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai · 2018
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Song Mei, Andrea Montanari, and Phan-Minh Nguyen · 2018
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Grant M Rotskoff and Eric Vanden-Eijnden · 2018
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Is robustness the cost of accuracy?–a comprehensive study on the robustness of 18 deep image classification models
Dong Su, Huan Zhang, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, and Yupeng Gao · 2018
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Feature-guided black-box safety testing of deep neural networks
Matthew Wicker, Xiaowei Huang, and Marta Kwiatkowska · 2018
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Nanyang Ye and Zhanxing Zhu · 2018
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Robustness quantification for classification with gaussian processes
Arno Blaas, Luca Laurenti, Andrea Patane, Luca Cardelli, Marta Kwiatkowska, and Stephen Roberts · 2019
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Modelling the influence of data structure on learning in neural networks
Sebastian Goldt, Marc Mézard, Florent Krzakala, and Lenka Zdeborová · 2019
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Uncertainty quantification with statistical guarantees in end-to-end autonomous driving control
Rhiannon Michelmore, Matthew Wicker, Luca Laurenti, Luca Cardelli, Yarin Gal, and Marta Kwiatkowska · 2019
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