21 citations · 87 across the 22 of their papers we have counts for
9 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…
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting
Xu Liu, Yutong Xia, Yuxuan Liang +7
Road traffic forecasting plays a critical role in smart city initiatives and has experienced significant advancements thanks to the power of deep learning in capturing non-linear p…
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…
Stimulative Training++: Go Beyond The Performance Limits of Residual Networks
Peng Ye, Tong He, Shengji Tang +4
Residual networks have shown great success and become indispensable in recent deep neural network models. In this work, we aim to re-investigate the training process of residual ne…
A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications
Can Li, Lei Bai, Lina Yao +2
Transportation is the backbone of the economy and urban development. Improving the efficiency, sustainability, resilience, and intelligence of transportation systems is critical an…
Spectrum-Guided Adversarial Disparity Learning
Zhe Liu, Lina Yao, Lei Bai +2
It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between…