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RESEARCH PAPER · Advances in parallel computing · 2020

Replicating Machine Learning Experiments in Materials Science

Line Catherine Pouchard, Yuewei Lin, Hubertus J. J. van Dam

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12 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
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42 direct citing works

Replicating Machine Learning Experiments in Materials ScienceOriginal2022 IEEE/ACM Workshop on Workflows in Support of Large-Scale Science (WORKS)1Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)14Challenges for Implementing FAIR Digital Objects with High Performance Workflows3Novel Proposals for FAIR, Automated, Recommendable, and Robust Workflows1A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows14Improving Reproducibility in AI Research: Four Mechanisms Adopted by JAIR9Conceptual Foundations for Scientific Audit Trails in Materials AI Systems0Reproducibility in machine‐learning‐based research: Overview, barriers, and drivers85Redefining "Reproducibility" in Data-Driven Materials Synthesis: Between Algorithmic and Experimental Replication0Measuring deep learning performance - an empirical study of performance distributions across architectures and tasks0The AI Reproducibility Index: Ranking Venues, Countries and Institutions by Reproducibility0Workflow-transfer benchmark of optical surface-roughness measurements against an internal tactile baseline across engineering materials and manufacturing processes0

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12 direct citing papers saved.

  1. 2022 IEEE/ACM Workshop on Workflows in Support of Large-Scale Science (WORKS)

    Unknown journal · 2022-01-01

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  2. Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

    Unknown journal · 2022-09-01

    Nicki L. Hickmon, Charuleka Varadharajan, Forrest M. Hoffman, Haruko Murakami Wainwright, Scott Collis

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  3. Challenges for Implementing FAIR Digital Objects with High Performance Workflows

    Research Ideas and Outcomes · 2022-10-12

    Line Catherine Pouchard, Tanzima Zerin Islam, Bogdan Nicolae

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  4. Novel Proposals for FAIR, Automated, Recommendable, and Robust Workflows

    Unknown journal · 2022-11-01

    Ishan Abhinit, Emily K. Adams, Khairul Alam, Brian Chase, Ewa Deelman, Lev Gorenstein, Stephen D. Hudson, Tanzima Zerin Islam, Jeffrey M. Larson, Geoffrey Lentner, Anirban Mandal, John-Luke Navarro, Bogdan Nicolae, Line Catherine Pouchard, Robert B. Ross, Banani Roy, Mats Rynge, Alexander Serebrenik, Karan Vahi, Stefan M. Wild, Yufeng Xin, Rafael Ferreira da Silva, Rosa Filgueira

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  5. A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows

    Digital Discovery · 2023-01-01

    Line Catherine Pouchard, Kristofer G. Reyes, Francis J. Alexander, Byung-Jun Yoon

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  6. Improving Reproducibility in AI Research: Four Mechanisms Adopted by JAIR

    Journal of Artificial Intelligence Research · 2024-12-27

    Odd Erik Gundersen, Malte Helmert, Holger H. Hoos

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  7. Conceptual Foundations for Scientific Audit Trails in Materials AI Systems

    Journal of Artificial Intelligence for Materials Science · 2025-01-01

    Luis Herrera, Daniela Rojas, Andres Castro

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  8. Reproducibility in machine‐learning‐based research: Overview, barriers, and drivers

    AI Magazine · 2025-04-14

    Harald Semmelrock, Tony Ross‐Hellauer, Simone Kopeinik, Dieter Theiler, Armin Haberl, Stefan Thalmann, Dominik Kowald

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  9. Redefining "Reproducibility" in Data-Driven Materials Synthesis: Between Algorithmic and Experimental Replication

    Journal of Computational and Data-Driven Materials Engineering · 2026-01-01

    Fatima Al-Zahra, Amina El Idrissi

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  10. Measuring deep learning performance - an empirical study of performance distributions across architectures and tasks

    Scientific Reports · 2026-05-04

    Kevin Coakley, Odd Erik Gundersen

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  11. The AI Reproducibility Index: Ranking Venues, Countries and Institutions by Reproducibility

    Unknown journal · 2026-07-20

    Kevin Coakley, Thijs Snelleman, Holger H. Hoos, Odd Erik Gundersen

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  12. Workflow-transfer benchmark of optical surface-roughness measurements against an internal tactile baseline across engineering materials and manufacturing processes

    Measurement Science and Technology · 2026-08-18

    D. Kucharski, Piotr Niesłony, Jolanta Beata Królczyk, Grzegorz Krolczyk, Katarzyna Nicińska, Natalia Wojciechowska, Łukasz Ślusarski, Michał Wieczorowski, Bartosz Gapiński

    Read original paper ↗ ·
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