Lecture
This lecture explores the concept of score-based generative models, focusing on the learning of natural distributions without a defined score. Topics include Langevin Monte Carlo algorithms, Hyvärinen's trick for learning the score, and the theoretical guarantees of sampling. The lecture also delves into the impact of neural network architecture on robustness, initialization strategies, and the trade-offs between width and depth in deep learning.