Yeah, I meant it in an informal way. Like, instead of each project pretending it's starting from scratch in its own universe, it was ok to connect results through priors. Uncertainty instead of p-value thresholds, open data where possible, reporting hyperparam scans as data
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This paper: Implementations are not specifications: Specification, replication and experimentation in computational cognitive modeling https://doi.org/10.1016/j.cogsys.2013.05.001 …
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So there are more than a single issue here but I have been discussing this usually on my own and often without any support from others since the academic year of 2009/2010.
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:( It's sad people don't see the importance of the points you've brought up. It's fascinating to see another field facing nearly the same issues as machine learning research and suggesting similar trajectories through them.
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Yup! Not only similar issues, but I think the "root" is the same and the crossover of people/ideas from the two areas is not a coincidence!
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I wanted to say something like, really subfields of the same field distinguished by emphasis placed on biological plausibility but thought that sounded to much like the kind of thing physicists say :p
- End of conversation
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