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Covers MuZero, a model that learns to predict rewards and actions iteratively, achieving state-of-the-art performance in board games and Atari video games.
Explores the challenges of robust vision, including distribution shifts, failure examples, and strategies for improving model robustness through diverse data pretraining.
Covers the practical implementation and applications of adversarial training, Generative Adversarial Networks, distance between distributions, and enforcing 1-Lipschitz in GANs.
