.@o_guest: Varieties of reproduciblity in empirical and computational domains #ICML2018 #ReproducibleML
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Guest: My work is mainly in computational cognitive neuroscience, so I've worked in CS and psychology l#ICML2018
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Guest: What is reproducibility? Why is it important? How can we achieve it?
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Guest: In empirical work, reproducibilty can mean running the same experiment and getting the same results, even in a different lab or with slightly different conditions (e.g. pigeons can categorize shapes and also colors).
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Guest: For software, reproducibility means running the same code and getting the same results (on the same machine or different machines) even years after the original analysis was run (my note: yesssss, longevity is so important!)
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Guest: In software, the goal for reproducibility is to be time- and machine-independent. But what about understanding, ideas and theories?
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Guest: In the ideal case, knowing the theory should be enough to allow you to get the same /similar results. Specification isn't code, comments or documentation.
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Guest: the high level understanding leads to the model which leads to the implementation (Guest & Cooper 2014)
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Guest: How do we achieve reproducibility? 1. Share code 2. Share data 3. Share the write up to communicate the high-level understanding
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Guest: I've also been working with the ReScience journal http://rescience.github.io/
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@ReScienceEds
And thank you so much for live tweeting me!
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Here are my slides if needed!
#ICML2018#ReproducibleMLhttps://figshare.com/articles/Varieties_of_Reproducibility_in_Empirical_and_Computational_Domains/6818018 …0 replies 6 retweets 9 likesThanks. Twitter will use this to make your timeline better. UndoUndo
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