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Cross-City Transfer Learning for Deep Spatio-Temporal Prediction
[article]
2018
arXiv
pre-print
Spatio-temporal prediction is a key type of tasks in urban computing, e.g., traffic flow and air quality. Adequate data is usually a prerequisite, especially when deep learning is adopted. However, the development levels of different cities are unbalanced, and still many cities suffer from data scarcity. To address the problem, we propose a novel cross-city transfer learning method for deep spatio-temporal prediction tasks, called RegionTrans. RegionTrans aims to effectively transfer knowledge
arXiv:1802.00386v2
fatcat:54wqpv4gzfbwbolnev653fxyia