Explores deep learning for autonomous vehicles, covering perception, action, and social forecasting in the context of sensor technologies and ethical considerations.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Explores trajectory forecasting in autonomous vehicles, focusing on deep learning models for predicting human trajectories in socially-aware transportation scenarios.
Explores predictive models and trackers for autonomous vehicles, covering object detection, tracking challenges, neural network-based tracking, and 3D pedestrian localization.
Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.
Delves into the geometric insights of deep learning models, exploring their vulnerability to perturbations and the importance of robustness and interpretability.
Covers the basics of reinforcement learning, including Markov Decision Processes and policy gradient methods, and explores real-world applications and recent advances.