ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility
Burak Yildiz
Hayley Hung
Jesse H. Krijthe
Cynthia C. S. Liem
Marco Loog
Gosia Migut
Frans Oliehoek
Annibale Panichella
P. Pawełczak
S. Picek
Mathijs de Weerdt
J. C. V. Gemert

Abstract
We present ReproducedPapers.org: an open online repository for teaching and structuring machine learning reproducibility. We evaluate doing a reproduction project among students and the added value of an online reproduction repository among AI researchers. We use anonymous self-assessment surveys and obtained 144 responses. Results suggest that students who do a reproduction project place more value on scientific reproductions and become more critical thinkers. Students and AI researchers agree that our online reproduction repository is valuable.
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