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, Przemyslaw Pawelczak,, Stjepan Picek, Mathijs de Weerdt, and Jan van Gemert
TL;DR
ReproducedPapers.org is an open online platform designed to enhance machine learning reproducibility by teaching, structuring, and evaluating reproducibility efforts among students and researchers, fostering critical thinking and valuing reproducibility.
Contribution
The paper introduces ReproducedPapers.org, an open repository that supports teaching and structuring ML reproducibility, and evaluates its educational and research impact.
Findings
Students engaging in reproduction projects value reproducibility more.
The online repository is considered valuable by students and researchers.
Reproduction projects enhance critical thinking among students.
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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