Covers the use of transformers in robotics, focusing on embodied perception and innovative applications in humanoid locomotion and reinforcement learning.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.