Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: **Is there a significant added value in comparison to the cited papers?**
Yes, authors provide evaluations at new datasets and new correlation analysis.

**Is this added value supported by accurate, convincing, and clear arguments?**
Yes, the main results presented at the end of the blog posts are well supported by evidence in this post. They are also convincing. I really liked the observation/statement about future research to focus on making proxies scale better with time (i.e. if evaluated later in the training they should bring better results).

# Strength
- See above

# Weakness
- Background on Zero-cost (ZC) proxies: I recommend pulling the definitions of the individual proxies from early paragraphs to under the classification. They are right now hidden in the paragraphs. It would best if you can enumerate all these proxies under relevant categories: i.e. data-dependent/free. This way reader can easily come back and find what each proxy calculates. I also recommend making this stylizing these abbreviations, see comment below.

# Minor
- `"number of parameters" perform just as well` and `"flops” and “params” are consistently`. I think these are refer to different proxies; but they are not defined yet in the intro. I would use longer descriptions here.
- Not sure different platforms pick the citations in blogposts, but would be nice to use latex-like bibliography managers when citing other work. I've seen another blog post that manged to do that like ar5iv does.
- I recommend authors to use a different typeface (like textsc) or styles like italic/bold/quote to denote proxies referred in the test. `synflow` in a sentence read like a word, not like a method.
- For correlation matrices, it might be best to use triangular formatting, no need to plot the upper triangular.
- `arbitrary heuristics such as grad_norm (taking the gradient norm of a randomly initialized architecture after one mini-batch),` I don't think that metric is arbitrary. One can show that high gradient norm would lead to lower training los under certain assumptions. 
- It would be probably better to have some symmetry in taxonomy: sth like `data-dependent` vs `data-free`. 

review_title: Review

Decision: Accept