5 papers
Dual-Label Learning With Irregularly Present Labels
Mingqian Li, Qiao Han, Ruifeng Li +2
In multi-task learning, labels are often missing irregularly across samples, which can be fully labeled, partially labeled or unlabeled. The irregular label presence often appears…
NPAT Null-Space Projected Adversarial Training Towards Zero Deterioration
Hanyi Hu, Qiao Han, Kui Chen +1
To mitigate the susceptibility of neural networks to adversarial attacks, adversarial training has emerged as a prevalent and effective defense strategy. Intrinsically, this counte…
Vertical Federated Learning Hybrid Local Pre-training
Wenguo Li, Xinling Guo, Xu Jiao +3
Vertical Federated Learning (VFL), which has a broad range of real-world applications, has received much attention in both academia and industry. Enterprises aspire to exploit more…
BlockEcho: Retaining Long-Range Dependencies for Imputing Block-Wise Missing Data
Qiao Han, Mingqian Li, Yao Yang +1
Block-wise missing data poses significant challenges in real-world data imputation tasks. Compared to scattered missing data, block-wise gaps exacerbate adverse effects on subseque…
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense
Qiao Han, yong huang, xinling Guo +3
Recent studies have revealed the vulnerability of Deep Neural Networks (DNNs) to adversarial examples, which can easily fool DNNs into making incorrect predictions. To mitigate thi…