8 papers · 1 filter
GeoMAE: Masking Representation Learning for Spatio-Temporal Graph Forecasting with Missing Values
Songyu Ke, Chenyu Wu, Yuxuan Liang +3
The ubiquity of missing data in urban intelligence systems, attributable to adverse environmental conditions and equipment failures, poses a significant challenge to the efficacy o…
Improving Open-world Continual Learning under the Constraints of Scarce Labeled Data
Yujie Li, Xiangkun Wang, Xin Yang +3
Open-world continual learning (OWCL) adapts to sequential tasks with open samples, learning knowledge incrementally while preventing forgetting. However, existing OWCL still requir…
Non-collective Calibrating Strategy for Time Series Forecasting
Bin Wang, Yongqi Han, Minbo Ma +4
Deep learning-based approaches have demonstrated significant advancements in time series forecasting. Despite these ongoing developments, the complex dynamics of time series make i…
AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks
Qiongyan Wang, Yutong Xia, Siru ZHong +6
Monitoring real-time air quality is essential for safeguarding public health and fostering social progress. However, the widespread deployment of air quality monitoring stations is…
Order-Robust Class Incremental Learning: Graph-Driven Dynamic Similarity Grouping
Guannan Lai, Yujie Li, Xiangkun Wang +3
Class Incremental Learning (CIL) aims to enable models to learn new classes sequentially while retaining knowledge of previous ones. Although current methods have alleviated catast…
UMOD: A Novel and Effective Urban Metro Origin-Destination Flow Prediction Method
Peng Xie, Minbo Ma, Bin Wang +2
Accurate prediction of metro Origin-Destination (OD) flow is essential for the development of intelligent transportation systems and effective urban traffic management. Existing ap…