14 citations · 29 across the 8 of their papers we have counts for
8 papers
Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations
Dongkun Wang, Wei Fan, Pengyang Wang +4
Urban traffic speed prediction aims to estimate the future traffic speed for improving the urban transportation services. Enormous efforts have been made on exploiting spatial corr…
Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning
Dongjie Wang, Lingfei Wu, Denghui Zhang +3
The essential task of urban planning is to generate the optimal land-use configuration of a target area. However, traditional urban planning is time-consuming and labor-intensive.…
Graph Soft-Contrastive Learning via Neighborhood Ranking
Zhiyuan Ning, Pengfei Wang, Pengyang Wang +5
Graph Contrastive Learning (GCL) has emerged as a promising approach in the realm of graph self-supervised learning. Prevailing GCL methods mainly derive from the principles of con…
Learning to Walk with Dual Agents for Knowledge Graph Reasoning
Denghui Zhang, Zixuan Yuan, Hao Liu +2
Graph walking based on reinforcement learning (RL) has shown great success in navigating an agent to automatically complete various reasoning tasks over an incomplete knowledge gra…
Cyberphysical Sequencing for Distributed Asset Management with Broad Traceability
Joshua Siegel, Gregory Falco
Cyber-Physical systems (CPS) have complex lifecycles involving multiple stakeholders, and the transparency of both hardware and software components' supply chain is opaque at best.…
RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training
Ziyue Qiao, Yanjie Fu, Pengyang Wang +5
With the growth of the academic engines, the mining and analysis acquisition of massive researcher data, such as collaborator recommendation and researcher retrieval, has become in…
Learning Disentangled Representations for Time Series
Yuening Li, Zhengzhang Chen, Daochen Zha +4
Time-series representation learning is a fundamental task for time-series analysis. While significant progress has been made to achieve accurate representations for downstream appl…
T-Net: A Semi-supervised Deep Model for Turbulence Forecasting
Denghui Zhang, Yanchi Liu, Wei Cheng +5
Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs. Traditional tur…