Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks

12 February 2018

Papers citing "Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks"

7 / 57 papers shown

Title
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks David J. Miller Zhen Xiang G. Kesidis AAML 6 35 0 12 Apr 2019
MMA Training: Direct Input Space Margin Maximization through Adversarial Training G. Ding Yash Sharma Kry Yik-Chau Lui Ruitong Huang AAML 16 270 0 06 Dec 2018
Invertible Residual Networks Jens Behrmann Will Grathwohl Ricky T. Q. Chen D. Duvenaud J. Jacobsen UQCV TPM 20 617 0 02 Nov 2018
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions Yusuke Tsuzuku Issei Sato AAML 14 62 0 11 Sep 2018
Motivating the Rules of the Game for Adversarial Example Research Justin Gilmer Ryan P. Adams Ian Goodfellow David G. Andersen George E. Dahl AAML 33 226 0 18 Jul 2018
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 226 1,835 0 03 Feb 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 261 3,109 0 04 Nov 2016