2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.SE2024
Context-Aware Fuzzing for Robustness Enhancement of Deep Learning Models
Haipeng Wang, Zhengyuan Wei, Qilin Zhou +1
In the testing-retraining pipeline for enhancing the robustness property of deep learning (DL) models, many state-of-the-art robustness-oriented fuzzing techniques are metric-orien…
cs.SE2024★ 2 cited
Delving into Parameter-Efficient Fine-Tuning in Code Change Learning: An Empirical Study
Shuo Liu, Jacky Keung, Zhen Yang +3
Compared to Full-Model Fine-Tuning (FMFT), Parameter Efficient Fine-Tuning (PEFT) has demonstrated superior performance and lower computational overhead in several code understandi…
cs.LG2023
A Majority Invariant Approach to Patch Robustness Certification for Deep Learning Models
Qilin Zhou, Zhengyuan Wei, Haipeng Wang +1
Patch robustness certification ensures no patch within a given bound on a sample can manipulate a deep learning model to predict a different label. However, existing techniques can…