3 citations · 4 across the 5 of their papers we have counts for
5 papers
Rethinking Agentic Reinforcement Learning In Large Language Models
Fangming Cui, Ruixiao Zhu, Cheng Fang +2
Reinforcement Learning (RL) has traditionally focused on training specialized agents to optimize predefined reward functions within narrowly defined environments. However, the adve…
Comprehensive Metapath-based Heterogeneous Graph Transformer for Gene-Disease Association Prediction
Wentao Cui, Shoubo Li, Chen Fang +4
Discovering gene-disease associations is crucial for understanding disease mechanisms, yet identifying these associations remains challenging due to the time and cost of biological…
TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes
Ran Su, Rui Shi, Hui Cui +4
Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performan…
Towards Graph Prompt Learning: A Survey and Beyond
Qingqing Long, Yuchen Yan, Peiyan Zhang +12
Large-scale "pre-train and prompt learning" paradigms have demonstrated remarkable adaptability, enabling broad applications across diverse domains such as question answering, imag…
Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations
Qingqing Long, Zheng Fang, Chen Fang +3
Traffic flow forecasting is a fundamental research issue for transportation planning and management, which serves as a canonical and typical example of spatial-temporal predictions…