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model interpretability

KAUST-CEMSE-CS-PhD-Dissertation-Defense-Xiaochuan-Gou-Explainability-and-Efficiency-in-Spatio-Temporal-Models

Explainability and Efficiency in Spatio-Temporal Models: Applications to Traffic Forecasting

Xiaochuan Gou, Ph.D. Student, Computer Science
Jul 6, 15:00 - 18:00

B5 L5 R5209

traffic forecasting Graph Neural Networks model interpretability

This dissertation addresses key challenges in deep learning-based traffic forecasting, including computational efficiency, model interpretability, and data limitations, despite recent progress in spatio-temporal modeling techniques.

Computer Science (CS)

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