There are lots of research papers available that are worth reading. We can read papers easily, but the associated code (not necessarily the official one developed by the authors of the paper) is often not available.

Papers with Code (and the associated Github repo) already lists many research papers and often there is a link to the associated Github repo with the code, but sometimes the code is missing. So, are there alternatives to Papers with Code (for such cases)?

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    $\begingroup$ I think it mainly depends on the authors if they want to open the source code, they should put it in the footnotes of their paper, or you can easily find it on Github or google. On the other hand, if the source code has too few stars on Github, you can't trust it, right? And paperswithcode also has the slack community where you can ask for code on there (paperswithcode.slack.com) $\endgroup$
    – CuCaRot
    Commented Oct 8, 2020 at 6:41
  • $\begingroup$ Thanks, your point makes question make more precise. Point is there are many GitHub repos which are implemented by the developer (not one who published paper) of older research paper. A nearby code also is helpful for starting work. Yes, Slack has community why stack exchange can't have the resources of its own? $\endgroup$
    – Posi2
    Commented Oct 8, 2020 at 7:26
  • $\begingroup$ what do you mean in this part "why stack exchange can't have the resources of its own?"? Do you want a summary of a good question? $\endgroup$
    – CuCaRot
    Commented Oct 8, 2020 at 7:31

2 Answers 2


Recently arxiv.org added a Code Tab towards the end of paper descriptions. Which contains links to both the official and community code.

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I don't know if this is the case for all the papers or not till know. But I'm sure it'll be extended to all the papers in a short while.

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    $\begingroup$ The Community Code actually is a list of links to "Papers with Code". $\endgroup$
    – nbro
    Commented Oct 9, 2020 at 11:46

Another good resource is the free CatalyzeX browser extension — it adds in-line links to any relevant code wherever you come across papers on various websites: AI/ML Papers with Code Everywhere - CatalyzeX

You can also see code results from CatalyzeX on arxiv.org in the Code tab towards the end of paper descriptions.

Full disclosure: I'm one of the creators. It's actively maintained and all feedback and requests are welcome!


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