Task: Language Identification
Input (Spanish, English): Summary: In this paper, the authors study ResNets through a theoretical formulation of a spin glass model. The conclusions are that ResNets behave as an ensemble of shallow networks at the start of training (by examining the magnitude of the weights for paths of a specific length) but this changes through training, through which the scaling parameter C (from assumption A4) increases, causing it to behave as an ensemble of deeper and deeper networks.  Clarity: This paper was somewhat difficult to follow, being heavy in notation, with perhaps some notation overloading. A summary of some of the proofs in the main text might have been helpful.  Specific Comments: - In the proof of Lemma 2, I'm not sure where the sequence beta comes from (I don't see how it follows from 11?)  - The ResNet structure used in the paper is somewhat different from normal with multiple layers being skipped? (Can the same analysis be used if only one layer is skipped? It seems like the skipping mostly affects the number of paths there are of a certain length?)  - The new experiments supporting the scale increase in practice are interesting! I'm not sure about Theorems 3, 4 necessarily proving this link theoretically however, particularly given the simplifying assumption at the start of Section 4.2?
Output (English): en