3 citations · 5 across the 9 of their papers we have counts for
9 papers
Fairness Mediator: Neutralize Stereotype Associations to Mitigate Bias in Large Language Models
Yisong Xiao, Aishan Liu, Siyuan Liang +2
LLMs have demonstrated remarkable performance across diverse applications, yet they inadvertently absorb spurious correlations from training data, leading to stereotype association…
BDefects4NN: A Backdoor Defect Database for Controlled Localization Studies in Neural Networks
Yisong Xiao, Aishan Liu, Xinwei Zhang +6
Pre-trained large deep learning models are now serving as the dominant component for downstream middleware users and have revolutionized the learning paradigm, replacing the tradit…
LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusions
Tianyuan Zhang, Lu Wang, Hainan Li +5
Lane detection (LD) is an essential component of autonomous driving systems, providing fundamental functionalities like adaptive cruise control and automated lane centering. Existi…
GenderBias-\emph{VL}: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing
Yisong Xiao, Aishan Liu, QianJia Cheng +6
Large Vision-Language Models (LVLMs) have been widely adopted in various applications; however, they exhibit significant gender biases. Existing benchmarks primarily evaluate gende…
RobustMQ: Benchmarking Robustness of Quantized Models
Yisong Xiao, Aishan Liu, Tianyuan Zhang +3
Quantization has emerged as an essential technique for deploying deep neural networks (DNNs) on devices with limited resources. However, quantized models exhibit vulnerabilities wh…
Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks
Jun Guo, Aishan Liu, Xingyu Zheng +4
Despite the broad application of Machine Learning models as a Service (MLaaS), they are vulnerable to model stealing attacks. These attacks can replicate the model functionality by…