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GTC Silicon Valley-2019 ID:S9806:Deep Learning For Spatiotemporal Data

Rose Yu(Northeastern University's Khoury College of Computer Sciences)
Applications such as climate science, intelligent transportation, aerospace control, and sports analytics apply machine learning for large-scale spatiotemporal data. This data is often nonlinear, high-dimensional, and demonstrates complex spatial and temporal correlation. Existing deep learning models cannot handle complex spatiotemporal dependency structures. We'll explain how to design deep learning models to learn from large-scale spatiotemporal data, especially for dealing with non-Euclidean geometry, long-term dependencies, and logical and physical constraints. We'll showcase the application of these models to problems such as long-term forecasting for transportation, long-range trajectories synthesis for sports analytics, and combating ground effect in quadcopter landing for aerospace control.l).

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