3 papers
cs.LG2026
W2T: LoRA Weights Already Know What They Can Do
Xiaolong Han, Ferrante Neri, Zijian Jiang +4
Each LoRA checkpoint compactly stores task-specific updates in low-rank weight matrices, offering an efficient way to adapt large language models to new tasks and domains. In princ…
cs.LG2026
A Survey of Weight Space Learning: Understanding, Representation, and Generation
Xiaolong Han, Zehong Wang, Bo Zhao +8
Neural network weights are typically viewed as the end product of training, while most deep learning research focuses on data, features, and architectures. However, recent advances…
cs.LG2022
Heterogeneous Graph Contrastive Multi-view Learning
Zehong Wang, Qi Li, Donghua Yu +3
Inspired by the success of contrastive learning (CL) in computer vision and natural language processing, graph contrastive learning (GCL) has been developed to learn discriminative…