ReproducedPapers.org

Openly Teaching and Structuring Machine Learning Reproducibility

Conference paper (2021)

Authors

B. Yildiz Pattern Recognition and Bioinformatics - EEMCS
H.S. Hung Pattern Recognition and Bioinformatics - EEMCS
M.M. de Weerdt Algorithmics - EEMCS
J.C. van Gemert Pattern Recognition and Bioinformatics - EEMCS
J.H. Krijthe Pattern Recognition and Bioinformatics - EEMCS
M. Loog Pattern Recognition and Bioinformatics - EEMCS
M.A. Migut Computer Science & Engineering-Teaching Team - EEMCS
F.A. Oliehoek Interactive Intelligence - EEMCS
A. Panichella Software Engineering - EEMCS
P. Pawełczak Embedded Systems - EEMCS
S. Picek Cyber Security - EEMCS
Copyright: © 2021 B. Yildiz, H.S. Hung, J.H. Krijthe, C.C.S. Liem, M. Loog, M.A. Migut, F.A. Oliehoek, A. Panichella, Przemysław Pawełczak, S. Picek, M.M. de Weerdt, J.C. van Gemert
DOI: https://doi.org/10.1007/978-3-030-76423-4_1
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Published Date

2021

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

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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