50 citations · 70 across the 14 of their papers we have counts for
14 papers
MultiFun-DAG: Multivariate Functional Directed Acyclic Graph
Tian Lan, Ziyue Li, Junpeng Lin +6
Directed Acyclic Graphical (DAG) models efficiently formulate causal relationships in complex systems. Traditional DAGs assume nodes to be scalar variables, characterizing complex…
Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling
Jiuyun Hu, Ziyue Li, Chen Zhang +2
Tensor clustering has become an important topic, specifically in spatio-temporal modeling, due to its ability to cluster spatial modes (e.g., stations or road segments) and tempora…
Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering
Ziyue Li, Hao Yan, Chen Zhang +3
Passenger clustering based on trajectory records is essential for transportation operators. However, existing methods cannot easily cluster the passengers due to the hierarchical s…
Relation-Aware Distribution Representation Network for Person Clustering with Multiple Modalities
Kaijian Liu, Shixiang Tang, Ziyue Li +4
Person clustering with multi-modal clues, including faces, bodies, and voices, is critical for various tasks, such as movie parsing and identity-based movie editing. Related method…
Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling
Ziyue Li, Yuchen Fang, You Li +7
A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU). However, i…
Tensor Dirichlet Process Multinomial Mixture Model for Passenger Trajectory Clustering
Ziyue Li, Hao Yan, Chen Zhang +4
Passenger clustering based on travel records is essential for transportation operators. However, existing methods cannot easily cluster the passengers due to the hierarchical struc…
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…
A Unified Probabilistic Framework for Spatiotemporal Passenger Crowdedness Inference within Urban Rail Transit Network
Min Jiang, Andi Wang, Ziyue Li +1
This paper proposes the Spatio-Temporal Crowdedness Inference Model (STCIM), a framework to infer the passenger distribution inside the whole urban rail transit (URT) system in rea…
MM-DAG: Multi-task DAG Learning for Multi-modal Data -- with Application for Traffic Congestion Analysis
Tian Lan, Ziyue Li, Zhishuai Li +6
This paper proposes to learn Multi-task, Multi-modal Direct Acyclic Graphs (MM-DAGs), which are commonly observed in complex systems, e.g., traffic, manufacturing, and weather syst…
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…
Domain Generalization using Pretrained Models without Fine-tuning
Ziyue Li, Kan Ren, Xinyang Jiang +3
Fine-tuning pretrained models is a common practice in domain generalization (DG) tasks. However, fine-tuning is usually computationally expensive due to the ever-growing size of pr…