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

This paper still considers the only resolution, depth and width dimensions, which have been studied in EfficientNet. Although the discovery in this paper that "resolution and depth are more important than width for tiny networks" is different from the conclusion in EfficientNet, I feel this point is not significant enough and it seems like just a supplement for EfficientNet. I'm not saying that this kind of method is not good, but I think the insights and intuitions why resolution and depth are more important than width for small networks (derived from this way) are still not clear. In my opinion, this paper is basically doing random search by shrinking the EfficientNet-B0 structure configurations on the mentioned three dimensions, I believe the derived observation is useful but the method itself contains very limited value to the community. Even some simple searching method like evolutionary searching can achieve similar or the same purpose through a more efficient way.