4 citations · 6 across the 13 of their papers we have counts for
5 papers · 1 filter
TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning
Xiaosong Han, Ke Chen, Xindi Dai +7
In real-world deployment, LLMs are often adapted continually across tasks to keep LLMs up-to-date in production, where new fine-tuning should preserve previously learned skills. Ho…
Advancing Graph Few-Shot Learning via In-Context Learning
Renchu Guan, Yajun Wang, Chunli Guo +5
Graph few-shot learning, which aims to classify nodes from novel classes with only a few labeled examples, is a widely studied problem in graph learning. However, existing methods…
Improving Graph Few-shot Learning with Hyperbolic Space and Denoising Diffusion
Yonghao Liu, Jialu Sun, Wei Pang +4
Graph few-shot learning, which focuses on effectively learning from only a small number of labeled nodes to quickly adapt to new tasks, has garnered significant research attention.…
Learning from Label Proportions with Dual-proportion Constraints
Tianhao Ma, Ximing Li, Changchun Li +1
Learning from Label Proportions (LLP) is a weakly supervised problem in which the training data comprise bags, that is, groups of instances, each annotated only with bag-level clas…
Harmful Visual Content Manipulation Matters in Misinformation Detection Under Multimedia Scenarios
Bing Wang, Ximing Li, Changchun Li +4
Nowadays, the widespread dissemination of misinformation across numerous social media platforms has led to severe negative effects on society. To address this challenge, the automa…