Explores Singular Value Decomposition and Principal Component Analysis for dimensionality reduction, with applications in visualization and efficiency.
Explores atomic descriptors, emphasizing symmetry, locality, and the challenges of incorporating electrostatics in machine learning models for chemistry.
Explores the challenges of robust vision, including distribution shifts, failure examples, and strategies for improving model robustness through diverse data pretraining.
