2 citations · 2 across the 8 of their papers we have counts for
6 papers · 1 filter
Impacts of Histories and Models on LLM Grading: A Study in Advanced Software Engineering Courses
Qilin Zhou, Zhuo Wang, Yue Li +1
Graduate-level research reading report assessment creates a substantial labor burden for educators. While large language models (LLMs) hold great potential for automating academic…
Toward Patch Robustness Certification and Detection for Deep Learning Systems Beyond Consistent Samples
Qilin Zhou, Zhengyuan Wei, Haipeng Wang +2
Patch robustness certification is an emerging kind of provable defense technique against adversarial patch attacks for deep learning systems. Certified detection ensures the detect…
A3Rank: Augmentation Alignment Analysis for Prioritizing Overconfident Failing Samples for Deep Learning Models
Zhengyuan Wei, Haipeng Wang, Qilin Zhou +1
Sharpening deep learning models by training them with examples close to the decision boundary is a well-known best practice. Nonetheless, these models are still error-prone in prod…
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…
CrossCert: A Cross-Checking Detection Approach to Patch Robustness Certification for Deep Learning Models
Qilin Zhou, Zhengyuan Wei, Haipeng Wang +2
Patch robustness certification is an emerging kind of defense technique against adversarial patch attacks with provable guarantees. There are two research lines: certified recovery…
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…