ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Dec 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 0; 2026: 2.
5 papers.
- 2024Reproducibility in Machine Learning-based Research: Overview, Barriers and Driverscited by 34
- 2023Reproducibility in Machine Learning-Driven Researchcited by 15
- 2021Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligencecited by 4
- 2026Make Mechanistic Interpretability Auditable: A Call to Develop Guidelines via Continuous Collaborative Reviewingcited by 2
- 2026Supporting Artifact Evaluation with LLMs: A Study with Published Security Research Paperscited by 1
- 2026Make Mechanistic Interpretability Auditable: A Call to Develop Guidelines via Continuous Collaborative Reviewing24 Apr 2026
- 2026Supporting Artifact Evaluation with LLMs: A Study with Published Security Research Papers6 Mar 2026
- 2024Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers20 Jun 2024
- 2023Reproducibility in Machine Learning-Driven Research19 Jul 2023
- 2021Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence1 Nov 2021