Course Description
Reading seminar covering active research in deep learning theory. Topics change annually. Recent years: neural tangent kernel regime and its breakdown for finite-width networks; feature learning vs. lazy training; in-context learning as implicit Bayesian inference; mechanistic interpretability of circuits; emergent capabilities and phase transitions; grokking and delayed generalization. Students present papers and contribute original research directions.