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Robert Stojnic
2020-04-07 20:31:21 +01:00
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@@ -20,7 +20,7 @@ The goals of the ML Code Completeness Checklist are to:
We find that those repositories that score higher on the checklist tend to have a higher number of GitHub stars.
We've verified this by analysing NeurIPS 2019 repositories. For more details on this analyis please refer to our [blog post](https://medium.com/paperswithcode/). We also provide the [data](notebooks/code_checklist-neurips2019.csv) and [notebook](notebooks/code_checklist-analysis.Rmd) to reproduce this analysis from the post.
We've verified this by analysing NeurIPS 2019 repositories. For more details on this analyis please refer to our [blog post](https://medium.com/paperswithcode/). We also provide the [data](notebooks/code_checklist-neurips2019.csv) and [notebook](notebooks/code_checklist-analysis.pdf) to reproduce this analysis from the post.
The checklist is made to be as general as possible. It consists of five items: