Openly Teaching and Structuring Machine Learning Reproducibility

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27 Downloads (Pure)


We present : 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.

Original languageEnglish
Title of host publicationReproducible Research in Pattern Recognition - 3rd International Workshop, RRPR 2021, Revised Selected Papers
EditorsBertrand Kerautret, Miguel Colom, Adrien Krähenbühl, Daniel Lopresti, Pascal Monasse, Hugues Talbot
PublisherSpringer Science+Business Media
Number of pages9
ISBN (Print)9783030764227
Publication statusPublished - 2021
Event3rd International Workshop on Reproducible Research in Pattern Recognition, RRPR 2021 - Virtual, Online
Duration: 11 Jan 202111 Jan 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12636 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Workshop on Reproducible Research in Pattern Recognition, RRPR 2021
CityVirtual, Online

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Machine learning
  • Online repository
  • Reproducibility


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