Reviews: The Description Length of Deep Learning models

Summary: The paper considers the problem of minimum description length of any model - resting on the conjecture (as supported by Solomonoff's general theory of inference and the Minimum Description Length) that a good model must compress the date along with its parameters well. Thus generalization is heuristically linked to compressive power of the model. Relating to the "Chaitin's hypothesis [Cha07] that "comprehension is compression" any regularity in the data can be exploited both to compress it and to make predictions", the authors focus only on the compression problem . Detailed Comments: Clarity: The paper in well written and presents a good survey of the related work. Originality: There is no theory in the paper.