24 citations · 24 across the 2 of their papers we have counts for
4 papers · 1 filter
Understanding Robustness in Teacher-Student Setting: A New Perspective
Zhuolin Yang, Zhaoxi Chen, Tiffany Cai +3
Adversarial examples have appeared as a ubiquitous property of machine learning models where bounded adversarial perturbation could mislead the models to make arbitrarily incorrect…
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
Micah Goldblum, Dimitris Tsipras, Chulin Xie +6
As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve…
Anomalous Example Detection in Deep Learning: A Survey
Saikiran Bulusu, Bhavya Kailkhura, Bo Li +2
Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detect…
Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?
Ruoxi Jia, Fan Wu, Xuehui Sun +6
Quantifying the importance of each training point to a learning task is a fundamental problem in machine learning and the estimated importance scores have been leveraged to guide a…