Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?
Authors: Ali Shafahi, Amin Ghiasi, Furong Huang, Tom Goldstein | Published: 2019-10-25
Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning
Authors: Mathieu N Galtier, Camille Marini | Published: 2019-10-25
Neurlux: Dynamic Malware Analysis Without Feature Engineering
Authors: Chani Jindal, Christopher Salls, Hojjat Aghakhani, Keith Long, Christopher Kruegel, Giovanni Vigna | Published: 2019-10-24
A Note on Our Submission to Track 4 of iDASH 2019
Authors: Marcel Keller, Ke Sun | Published: 2019-10-24
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection Technique
Authors: Muhammad Furqan Rafique, Muhammad Ali, Aqsa Saeed Qureshi, Asifullah Khan, Anwar Majid Mirza | Published: 2019-10-24 | Updated: 2020-12-26
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
Authors: Wei Deng, Xiao Zhang, Faming Liang, Guang Lin | Published: 2019-10-23 | Updated: 2020-04-13
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks
Authors: Alexander Levine, Soheil Feizi | Published: 2019-10-23
A Useful Taxonomy for Adversarial Robustness of Neural Networks
Authors: Leslie N. Smith | Published: 2019-10-23
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors
Authors: Ivan Homoliak, Petr Hanacek | Published: 2019-10-23
A Context-aware Framework for Detecting Sensor-based Threats on Smart Devices
Authors: Amit Kumar Sikder, Hidayet Aksu, A. Selcuk Uluagac | Published: 2019-10-22