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

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.