3 citations · 7 across the 13 of their papers we have counts for
6 papers · 1 filter
Hyperbolic Continuous Structural Entropy for Hierarchical Clustering
Guangjie Zeng, Hao Peng, Angsheng Li +5
Hierarchical clustering is a fundamental machine-learning technique for grouping data points into dendrograms. However, existing hierarchical clustering methods encounter two prima…
SeSE: Black-Box Uncertainty Quantification for Large Language Models Based on Structural Information Theory
Xingtao Zhao, Hao Peng, Dingli Su +4
Reliable uncertainty quantification (UQ) is essential for deploying large language models (LLMs) in safety-critical scenarios, as it enables them to abstain from responding when un…
STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation
Yiming Wang, Hao Peng, Senzhang Wang +4
Traffic data imputation is fundamentally important to support various applications in intelligent transportation systems such as traffic flow prediction. However, existing time-to-…
Unsupervised Graph Clustering with Deep Structural Entropy
Jingyun Zhang, Hao Peng, Li Sun +3
Research on Graph Structure Learning (GSL) provides key insights for graph-based clustering, yet current methods like Graph Neural Networks (GNNs), Graph Attention Networks (GATs),…
ASIL: Augmented Structural Information Learning for Deep Graph Clustering in Hyperbolic Space
Li Sun, Zhenhao Huang, Yujie Wang +4
Graph clustering is a longstanding topic in machine learning. Recently, deep methods have achieved results but still require predefined cluster numbers K and struggle with imbalanc…
Motion Forecasting for Autonomous Vehicles: A Survey
Jianxin Shi, Jinhao Chen, Yuandong Wang +4
In recent years, the field of autonomous driving has attracted increasingly significant public interest. Accurately forecasting the future behavior of various traffic participants…