Efficient Deep Learning in Irregular Spatio-Temporal Tasks

This thesis works towards improving the efficiency of the deep learning process for irregular spatio-temporal tasks. Efficiency is an increasinly important issue as deep learning is applied to more complex and large-scale domains. This thesis outlines key techniques to reduce the cost of the dataset, model training, and model inference. These techniques are evaluated with data gathered from video games and autonomous vehicles.