1 citations · 1 across the 4 of their papers we have counts for
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UrbanFM: Scaling Urban Spatio-Temporal Foundation Models
Wei Chen, Yuqian Wu, Junle Chen +2
Urban systems, as dynamic complex systems, continuously generate spatio-temporal data streams that encode the fundamental laws of human mobility and city evolution. While AI for Sc…
Learning from Complexity: Exploring Dynamic Sample Pruning of Spatio-Temporal Training
Wei Chen, Junle Chen, Yuqian Wu +2
Spatio-temporal forecasting is fundamental to intelligent systems in transportation, climate science, and urban planning. However, training deep learning models on the massive, oft…
Test-Time Learning of Causal Structure from Interventional Data
Wei Chen, Rui Ding, Bojun Huang +5
Supervised causal learning has shown promise in causal discovery, yet it often struggles with generalization across diverse interventional settings, particularly when intervention…
Select, then Balance: Exploring Exogenous Variable Modeling of Spatio-Temporal Forecasting
Wei Chen, Yuqian Wu, Yuanshao Zhu +4
Spatio-temporal (ST) forecasting is critical for dynamic systems, yet existing methods predominantly rely on modeling a limited set of observed target variables. In this paper, we…
Learning with Calibration: Exploring Test-Time Computing of Spatio-Temporal Forecasting
Wei Chen, Yuxuan Liang
Spatio-temporal forecasting is crucial in many domains, such as transportation, meteorology, and energy. However, real-world scenarios frequently present challenges such as signal…
Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting
Wei Chen, Yuxuan Liang
The widespread deployment of sensing devices leads to a surge in data for spatio-temporal forecasting applications such as traffic flow, air quality, and wind energy. Although spat…