Adversarial Phenomenon in the Eyes of Bayesian Deep Learning
Authors: Ambrish Rawat, Martin Wistuba, Maria-Irina Nicolae | Published: 2017-11-22
Generative Adversarial Positive-Unlabelled Learning
Authors: Ming Hou, Brahim Chaib-draa, Chao Li, Qibin Zhao | Published: 2017-11-21 | Updated: 2018-04-04
AndroVault: Constructing Knowledge Graph from Millions of Android Apps for Automated Analysis
Authors: Guozhu Meng, Yinxing Xue, Jing Kai Siow, Ting Su, Annamalai Narayanan, Yang Liu | Published: 2017-11-20 | Updated: 2017-11-21
Evaluating Robustness of Neural Networks with Mixed Integer Programming
Authors: Vincent Tjeng, Kai Xiao, Russ Tedrake | Published: 2017-11-20 | Updated: 2019-02-18
Model Extraction Warning in MLaaS Paradigm
Authors: Manish Kesarwani, Bhaskar Mukhoty, Vijay Arya, Sameep Mehta | Published: 2017-11-20
Hardening Quantum Machine Learning Against Adversaries
Authors: Nathan Wiebe, Ram Shankar Siva Kumar | Published: 2017-11-17
Enhanced Attacks on Defensively Distilled Deep Neural Networks
Authors: Yujia Liu, Weiming Zhang, Shaohua Li, Nenghai Yu | Published: 2017-11-16
The best defense is a good offense: Countering black box attacks by predicting slightly wrong labels
Authors: Yannic Kilcher, Thomas Hofmann | Published: 2017-11-15
CryptoDL: Deep Neural Networks over Encrypted Data
Authors: Ehsan Hesamifard, Hassan Takabi, Mehdi Ghasemi | Published: 2017-11-14
Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples
Authors: Jihun Hamm, Akshay Mehra | Published: 2017-11-12 | Updated: 2018-06-27