Thanks, that’s a good point! I was very uncertain about that, it was mostly a made-up number. I do think the time to implement an ML paper depends wildly on how complex the paper is (e.g. a new training algorithm paper necessitates a lot more time to test it than a post-hoc interpretability paper that uses pre-trained models) and how much you implement (e.g. rewrite the code but don’t do any training vs evaluate the key result to get the most important graph vs try to replicate almost all of the results).
I now think my original 10-20 hours per paper number was probably an underestimate, but it feels really hard to come up with a robust estimate here and I’m not sure how valuable it would be, so I’ve removed that parenthetical from the text.
Thanks, that’s a good point! I was very uncertain about that, it was mostly a made-up number. I do think the time to implement an ML paper depends wildly on how complex the paper is (e.g. a new training algorithm paper necessitates a lot more time to test it than a post-hoc interpretability paper that uses pre-trained models) and how much you implement (e.g. rewrite the code but don’t do any training vs evaluate the key result to get the most important graph vs try to replicate almost all of the results).
I now think my original 10-20 hours per paper number was probably an underestimate, but it feels really hard to come up with a robust estimate here and I’m not sure how valuable it would be, so I’ve removed that parenthetical from the text.