Delves into the geometric insights of deep learning models, exploring their vulnerability to perturbations and the importance of robustness and interpretability.
Explores uncertainty quantification and label error detection in deep learning for semantic segmentation, focusing on challenges and methods for error detection.
Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.