Explores the challenges of robust vision, including distribution shifts, failure examples, and strategies for improving model robustness through diverse data pretraining.
Examines the generalization of ImageNet classifiers, safety-critical applications, overfitting, and the reliability of machine learning models.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Delves into score-based generative models, exploring learning natural distributions and the impact of neural network architecture on robustness.
Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.
Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.
