Reviews: Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks

Originality: To the best of my knowledge, the results are novel and provide important extensions/improvements over the previous art. Quality: I did a high level check of the proofs and it seems sound to me. Clarity: the paper is a joy to read. The problem definition, assumptions, the algorithm, and statement of results are very well presented. Significance: the results provide several extensions and improvements over the previous work, including training deeper models, training all layers, training with SGD (rather than GD), and smaller required overparameterization.