Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 1: Reject
review_content: This paper proposes a new graphon-based search space. Unlike most other NAS works that search for exact network structures, this paper aims to search for the random graph distribution with graphon. Overall, it provides some new angles for NAS search space design, but the experimental results are very weak.

1.  It simply ignore all other NAS works and just compares with the baseline DenseNet and random deletion/walk (WS-G). Despite that, the gain (accuracy +0.17% than DenseNet baseline) is very marginal compared to other approaches:  random-wire (accuracy +2% than resent50 baseline), FBNet (accuracy +2% than MobileNetv2 baseline).
2. According to Section 5.1, the search is performed on CIFAR-10, but there is no evaluation on CIFAR-10 at all. The only results are reported for ImageNet instead, which is kind of strange.

Given these weak results, I cannot accept this paper in the current form.
review_title: Official Blind Review #2

final_score: rating: 3: Weak Reject
review_content: The authors propose a new search space based on graphons and explore some of its benefits such as certain theoretical properties. The architecture search shares similarities with DARTS. An important difference is that the network parameters are not shared.
The paper is well-written and the authors consider that the typical reader will not be familiar with graphons. I agree that their proposed model allows for more architectures but in practice it is not much stronger than WS-G. The argumentation with respect to parameters is unclear to me. On one hand, you manually influence the number of parameters, on the other you argue that you use less parameters. Obviously, you chose that your baselines have more parameters. How do results for WS-G look like if you reduce its parameters to match yours? In fact, you were searching for an architecture on CIFAR-10 but you did not report your results here. Instead you only report your transferred results to ImageNet. Is it possible that you also report results on CIFAR-10? Finally, you do not discuss that your graph contains only one kind of node. In many NAS methods the search space contains various types of operations. Do you think this is a problem? Is there a trivial way to extend your method to cover this as well?
review_title: Official Blind Review #1

Meta-review: This paper proposes a graphon-based search space for neural architecture search. Unfortunately, the paper as currently stands and the small effect sizes in the experimental results raise questions about the merits of actually employing such a search space for the specific task of NAS. The reviewers expressed concerns that the results do not convincingly support graphon being a superior search space as claimed in the paper. 

Decision: Reject