101 citations · 193 across the 17 of their papers we have counts for
6 papers · 1 filter
UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation
Du Yin, Hao Xue, Jinliang Deng +4
In time-series generation, existing approaches typically handcraft ortrain a separate model for each dataset, which hinders their scalability and fails to leverage shared temporal…
HiT-JEPA: A Hierarchical Self-supervised Trajectory Embedding Framework for Similarity Computation
Lihuan Li, Hao Xue, Shuang Ao +2
The representation of urban trajectory data plays a critical role in effectively analyzing spatial movement patterns. Despite considerable progress, the challenge of designing traj…
Double-Diffusion: Balancing Speed, Accuracy, and Uncertainty in Probabilistic Forecasting for Urban Sensor Networks
Hanlin Dong, Arian Prabowo, Hao Xue +5
Urban sensor networks need forecasts that are accurate, carry useful uncertainty, and refresh fast enough to act on as new readings arrive. These goals conflict: deterministic mode…
Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
Du Yin, Jinliang Deng, Shuang Ao +6
Training models on spatio-temporal (ST) data poses an open problem due to the complicated and diverse nature of the data itself, and it is challenging to ensure the model's perform…
XXLTraffic: Expanding and Extremely Long Traffic forecasting beyond test adaptation
Du Yin, Hao Xue, Arian Prabowo +2
Traffic forecasting is crucial for smart cities and intelligent transportation initiatives, where deep learning has made significant progress in modeling complex spatio-temporal pa…
Two Sides of Miscalibration: Identifying Over and Under-Confidence Prediction for Network Calibration
Shuang Ao, Stefan Rueger, Advaith Siddharthan
Proper confidence calibration of deep neural networks is essential for reliable predictions in safety-critical tasks. Miscalibration can lead to model over-confidence and/or under-…