ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility
This work addresses the problem of teaching and structuring machine learning reproducibility for students and AI researchers, providing a platform to facilitate this process.
This paper introduces ReproducedPapers.org, an open online repository designed to teach and structure machine learning reproducibility. Through surveys with 144 responses, it found that students undertaking reproduction projects developed a greater appreciation for scientific reproductions and improved critical thinking skills.
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.