50 citations · 70 across the 14 of their papers we have counts for
5 papers · 1 filter
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping
Luxuan Wang, Lei Bai, Ziyue Li +2
Correlated time series analysis plays an important role in many real-world industries. Learning an efficient representation of this large-scale data for further downstream tasks is…
Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting
Yirong Chen, Ziyue Li, Wanli Ouyang +1
Accurate traffic forecasting is vital to intelligent transportation systems, which are widely adopted to solve urban traffic issues. Existing traffic forecasting studies focus on m…
Dynamic Causal Graph Convolutional Network for Traffic Prediction
Junpeng Lin, Ziyue Li, Zhishuai Li +3
Modeling complex spatiotemporal dependencies in correlated traffic series is essential for traffic prediction. While recent works have shown improved prediction performance by usin…
Towards Inference Efficient Deep Ensemble Learning
Ziyue Li, Kan Ren, Yifan Yang +3
Ensemble methods can deliver surprising performance gains but also bring significantly higher computational costs, e.g., can be up to 2048X in large-scale ensemble tasks. However,…
Jointly Contrastive Representation Learning on Road Network and Trajectory
Zhenyu Mao, Ziyue Li, Dedong Li +2
Road network and trajectory representation learning are essential for traffic systems since the learned representation can be directly used in various downstream tasks (e.g., traff…