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cs.LG2025
A Gravity-informed Spatiotemporal Transformer for Human Activity Intensity Prediction
Yi Wang, Zhenghong Wang, Fan Zhang +9
Human activity intensity prediction is crucial to many location-based services. Despite tremendous progress in modeling dynamics of human activity, most existing methods overlook p…
cs.LG2025★ 4 cited
RegionGCN: Spatial-Heterogeneity-Aware Graph Convolutional Networks
Hao Guo, Han Wang, Di Zhu +3
Modeling spatial heterogeneity in the data generation process is essential for understanding and predicting geographical phenomena. Despite their prevalence in geospatial tasks, ne…
cs.LG2023
Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks
Namid R. Stillman, Rory Baggott, Justin Lyon +4
The ability to construct a realistic simulator of financial exchanges, including reproducing the dynamics of the limit order book, can give insight into many counterfactual scenari…