papers
Publications (12)
cs.LG2025
Str-GCL: Structural Commonsense Driven Graph Contrastive Learning
Dongxiao He, Yongqi Huang, Jitao Zhao +2
cs.LG2025
DeRS: Towards Extremely Efficient Upcycled Mixture-of-Experts Models
Yongqi Huang, Peng Ye, Chenyu Huang +5
cs.CV2023
Merging Vision Transformers from Different Tasks and Domains
Peng Ye, Chenyu Huang, Mingzhu Shen +4
cs.LG2026
MUG: Meta-path-aware Universal Heterogeneous Graph Pre-Training
Lianze Shan, Jitao Zhao, Dongxiao He +3
cs.CV2023
Experts Weights Averaging: A New General Training Scheme for Vision Transformers
Yongqi Huang, Peng Ye, Xiaoshui Huang +4
cs.LG2026
GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks
Dongxiao He, Wenxuan Sun, Yongqi Huang +2
cs.LG2025
Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative Sampling
Yongqi Huang, Jitao Zhao, Dongxiao He +3
cs.LG2025
One Prompt Fits All: Universal Graph Adaptation for Pretrained Models
Yongqi Huang, Jitao Zhao, Dongxiao He +5
cs.CV2024
Adapter-X: A Novel General Parameter-Efficient Fine-Tuning Framework for Vision
Minglei Li, Peng Ye, Yongqi Huang +5
cs.CV2023
Partial Fine-Tuning: A Successor to Full Fine-Tuning for Vision Transformers
Peng Ye, Yongqi Huang, Chongjun Tu +4
cs.LG2026
CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts
Peiyuan Li, Yongqi Huang, Jitao Zhao +3
cs.LG2026
Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding
Chundong Liang, Yongqi Huang, Dongxiao He +4