3 citations · 7 across the 13 of their papers we have counts for
13 papers
Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization
Jiecong Wang, Hao Peng, Chunyang Liu +2
Chain-of-Thought (CoT) empowers Large Language Models (LLMs) to tackle complex problems, but remains constrained by the computational cost and early token commitments in discrete r…
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…
Structural Entropy Guided Probabilistic Coding
Xiang Huang, Hao Peng, Li Sun +4
Probabilistic embeddings have several advantages over deterministic embeddings as they map each data point to a distribution, which better describes the uncertainty and complexity…
LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering
Li Sun, Zhenhao Huang, Hao Peng +3
Graph clustering is a fundamental problem in machine learning. Deep learning methods achieve the state-of-the-art results in recent years, but they still cannot work without predef…
Semi-Supervised Clustering via Structural Entropy with Different Constraints
Guangjie Zeng, Hao Peng, Angsheng Li +4
Semi-supervised clustering techniques have emerged as valuable tools for leveraging prior information in the form of constraints to improve the quality of clustering outcomes. Desp…
MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization
Dongcheng Zou, Senzhang Wang, Xuefeng Li +5
Traffic forecasting is a complex multivariate time-series regression task of paramount importance for traffic management and planning. However, existing approaches often struggle t…
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs
Guangjie Zeng, Hao Peng, Angsheng Li +4
Skin lesion segmentation is a fundamental task in dermoscopic image analysis. The complex features of pixels in the lesion region impede the lesion segmentation accuracy, and exist…
Hierarchical State Abstraction Based on Structural Information Principles
Xianghua Zeng, Hao Peng, Angsheng Li +3
State abstraction optimizes decision-making by ignoring irrelevant environmental information in reinforcement learning with rich observations. Nevertheless, recent approaches focus…