Review for NeurIPS paper: Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets

The paper received mixed ratings: two reviewers recommend acceptance, and two reviewers consider the paper is marginally below the threshold. All reviewers agree that the paper provides useful insights, e.g., the observation that resolution and depth are more important than width for tiny networks. The main concerns raised by the reviewers were (i) novelty is not highly significant/the method is too heuristic (ii) issues with experiments and lack of analysis on other tasks, such as object detection. The rebuttal helped clarify several other questions raised by the reviewers, and included new experiments on COCO object detection using Faster-RCNN. All reviewers actively participated in the discussion phase.