collaborators

6 papers

cs.LG2025

MetaSTH-Sleep: Towards Effective Few-Shot Sleep Stage Classification for Health Management with Spatial-Temporal Hypergraph Enhanced Meta-Learning

Jingyu Li, Tiehua Zhang, Jinze Wang +6

Accurate classification of sleep stages based on bio-signals is fundamental not only for automatic sleep stage annotation, but also for clinical health management and continuous sl…

cs.MM2025

Multimodal Fusion via Hypergraph Autoencoder and Contrastive Learning for Emotion Recognition in Conversation

Zijian Yi, Ziming Zhao, Zhishu Shen +1

Multimodal emotion recognition in conversation (MERC) seeks to identify the speakers' emotions expressed in each utterance, offering significant potential across diverse fields. Th…

cs.MA2025

DHLight: Multi-agent Policy-based Directed Hypergraph Learning for Traffic Signal Control

Zhen Lei, Zhishu Shen, Kang Wang +2

Recent advancements in Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNNs) have demonstrated notable promise in the realm of intelligent traffic signal control, faci…

cs.LG2025

CHASE: A Causal Hypergraph based Framework for Root Cause Analysis in Multimodal Microservice Systems

Ziming Zhao, Zhenwei Wang, Tiehua Zhang +7

In recent years, the widespread adoption of distributed microservice architectures within the industry has significantly increased the demand for enhanced system availability and r…

cs.MA2025

Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal Hypergraphs

Kang Wang, Zhishu Shen, Zhen Lei +1

Traffic signal control systems (TSCSs) are integral to intelligent traffic management, fostering efficient vehicle flow. Traditional approaches often simplify road networks into st…

cs.LG2024

Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

Tiehua Zhang, Yuze Liu, Zhishu Shen +4

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learni…