Fetching the paper…
Reading the bibliography…
Machine learning (ML) classification is increasingly used in safety-critical systems.
Mode-finding for mixtures of Gaussian distributions
Miguel A. Carreira-Perpinan · 2000
Earlier work this paper cites.
Sparse Gaussian processes using pseudo-inputs
Edward Snelson and Zoubin Ghahramani · 2006
Earlier work this paper cites.
Gaussian processes for machine learning
Christopher KI Williams and Carl Edward Rasmussen · 2006
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Srndic, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
Earlier work this paper cites.
Deep Gaussian processes
Andreas Damianou and Neil Lawrence · 2013
Earlier work this paper cites.
A continuation approach to mode-finding of multivariate Gaussian mixtures and kernel density estimates
Seppo Pulkkinen, Marko Mikael Mäkelä, and Napsu Karmitsa · 2013
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.
The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami · 2016
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Provably minimally-distorted adversarial examples
Nicholas Carlini, Guy Katz, Clark Barrett, and David L Dill · 2017
Cited alongside, same era.
Yes, machine learning can be more secure! A case study on Android malware detection
Ambra Demontis, Marco Melis, Battista Biggio, Davide Maiorca, Daniel Arp, Konrad Rieck, Igino Corona, Giorgio Giacinto, and Fabio Roli · 2017
Cited alongside, same era.
Lower bounds on the robustness to adversarial perturbations
Jonathan Peck, Joris Roels, Bart Goossens, and Yvan Saeys · 2017
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Later among the works it cites.
Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Andrew Slavin Ross and Finale Doshi-Velez · 2018
Later among the works it cites.
Rogue signs: Deceiving traffic sign recognition with malicious ads and logos
Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Prateek Mittal, and Mung Chiang · 2018
Later among the works it cites.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 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.
Safety verification of deep neural networks
Xiaowei Huang, Marta Kwiatkowska, Sen Wang, and Min Wu · 2017
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner
Cited in the paper.
The limitations of model uncertainty in adversarial settings
Kathrin Grosse, David Pfaff, Michael T Smith, and Michael Backes
Cited in the paper.
Killing four birds with one Gaussian process: Analyzing test-time attack vectors on classification
Kathrin Grosse, Michael T Smith, and Michael Backes
Cited in the paper.
Robustness guarantees for Bayesian inference with Gaussian processes
Luca Cardelli, Marta Kwiatkowska, Luca Laurenti, and Andrea Patane · 2019
Closest in time.
One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai · 2019
Closest in time.
The limitations of adversarial training and the blind-spot attack
Huan Zhang, Hongge Chen, Zhao Song, Duane S. Boning, Inderjit S. Dhillon, and Cho-Jui Hsieh · 2019
Closest in time.