2 papers
cs.LG2026
Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms
Bo Wang, Jia Ni, Mengnan Zhao +2
The unauthorized use of personal data in model training has emerged as a growing privacy threat. Unlearnable examples (UEs) address this issue by embedding imperceptible perturbati…
cs.LG2025
HGMP:Heterogeneous Graph Multi-Task Prompt Learning
Pengfei Jiao, Jialong Ni, Di Jin +4
The pre-training and fine-tuning methods have gained widespread attention in the field of heterogeneous graph neural networks due to their ability to leverage large amounts of unla…