ExAD: An Ensemble Approach for Explanation-based Adversarial Detection

no code implementations • 22 Mar 2021 • Raj Vardhan, Ninghao Liu, Phakpoom Chinprutthiwong, Weijie Fu, Zhenyu Hu, Xia Ben Hu, Guofei Gu

Several defense methods have been proposed against adversarial attacks to detect adversarial examples at test time or to make machine learning models more robust.

BIG-bench Machine Learning

A Survey on Large-scale Machine Learning

1 code implementation • 10 Aug 2020 • Meng Wang, Weijie Fu, Xiangnan He, Shijie Hao, Xindong Wu

Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems.

BIG-bench Machine Learning Computational Efficiency +1

Distribution-Guided Local Explanation for Black-Box Classifiers

no code implementations • 25 Sep 2019 • Weijie Fu, Meng Wang, Mengnan Du, Ninghao Liu, Shijie Hao, Xia Hu

Existing local explanation methods provide an explanation for each decision of black-box classifiers, in the form of relevance scores of features according to their contributions.