Reproscreener: Leveraging LLMs for Assessing Computational Reproducibility of Machine Learning Pipelines-Reference-Cited by-同舟云学术

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Reproscreener: Leveraging LLMs for Assessing Computational Reproducibility of Machine Learning Pipelines

Published:2024-06-18 Issue: Volume:10 Page:101-109
ISSN:
Container-title:Proceedings of the 2nd ACM Conference on Reproducibility and Replicability
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Author:

Bhaskar Adhithya¹^ORCID,Stodden Victoria¹^ORCID

Affiliation:

1. University of Southern California, United States

Funder

National Science Foundation

The Center for Research and Education in AI and Learning (REAL@USC)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3641525.3663629

Reference35 articles.

1. 2019. Sharing high expectations. 1, 8 (2019), 329–329. Number: 8 Publisher: Nature Publishing Group.

2. David B. Allison, Richard M. Shiffrin, and Victoria Stodden. 2018. Reproducibility of research: Issues and proposed remedies. 115, 11 (2018), 2561–2562. Publisher: Proceedings of the National Academy of Sciences.

3. Iz Beltagy Kyle Lo and Arman Cohan. 2019. SciBERT: A Pretrained Language Model for Scientific Text. arxiv:1903.10676 [cs]

4. Adhithya Bhaskar and Victoria Stodden. 2024. Reproscreener.

5. Aymeric Blaizot, Sajesh K. Veettil, Pantakarn Saidoung, Carlos Francisco Moreno-Garcia, Nirmalie Wiratunga, Magaly Aceves-Martins, Nai Ming Lai, and Nathorn Chaiyakunapruk. 2022. Using artificial intelligence methods for systematic review in health sciences: A systematic review. 13, 3 (2022), 353–362.

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