3 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 3 cited
Good-looking but Lacking Faithfulness: Understanding Local Explanation Methods through Trend-based Testing
Jinwen He, Kai Chen, Guozhu Meng +2
While enjoying the great achievements brought by deep learning (DL), people are also worried about the decision made by DL models, since the high degree of non-linearity of DL mode…
cs.SE2023
ConFL: Constraint-guided Fuzzing for Machine Learning Framework
Zhao Liu, Quanchen Zou, Tian Yu +4
As machine learning gains prominence in various sectors of society for automated decision-making, concerns have risen regarding potential vulnerabilities in machine learning (ML) f…
cs.SE2023★ 2 cited
ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning
Shangqing Liu, Bozhi Wu, Xiaofei Xie +2
Large-scale pre-trained models such as CodeBERT, GraphCodeBERT have earned widespread attention from both academia and industry. Attributed to the superior ability in code represen…