4 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.CL2022★ 4 cited
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement
Yuting Yang, Pei Huang, Juan Cao +5
Recent years have seen the wide application of NLP models in crucial areas such as finance, medical treatment, and news media, raising concerns of the model robustness and vulnerab…
cs.CL2022★ 1 cited
Quantifying Robustness to Adversarial Word Substitutions
Yuting Yang, Pei Huang, FeiFei Ma +4
Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations. Before they are widely adopted, the fundamental issues of robustness need to be addres…
cs.LG2021
ε-weakened Robustness of Deep Neural Networks
Pei Huang, Yuting Yang, Minghao Liu +3
This paper introduces a notation of -weakened robustness for analyzing the reliability and stability of deep neural networks (DNNs). Unlike the conventional robustness…