Skip to content

Bibliography

  1. Bhaskar, A. and Stodden, V. 2024. Reproscreener: Leveraging LLMs for Assessing Computational Reproducibility of Machine Learning Pipelines. Proceedings of the 2nd ACM Conference on Reproducibility and Replicability (New York, NY, USA, Jul. 2024), 101--109. ↩

  2. Bhaskar, A. and Stodden, V. 2022. ReproScreen: Enabling Robustness in Machine Learning at Scale via Automated Knowledge Verification. Zenodo. ↩

  3. Krafczyk, M.S. et al. 2021. Learning from reproducing computational results: Introducing three principles and the Reproduction Package. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 379, 2197 (May 2021), rsta.2020.0069, 20200069. DOI:https://doi.org/10.1098/rsta.2020.0069. ↩

  4. Krafczyk, M. et al. 2019. Scientific Tests and Continuous Integration Strategies to Enhance Reproducibility in the Scientific Software Context. Proceedings of the 2nd International Workshop on Practical Reproducible Evaluation of Computer Systems - P-RECS '19 (Phoenix, AZ, USA, 2019), 23--28. ↩

  5. Stodden, V. et al. 2018. Enabling the Verification of Computational Results: An Empirical Evaluation of Computational Reproducibility. Proceedings of the First International Workshop on Practical Reproducible Evaluation of Computer Systems (Tempe AZ USA, 2018), 1--5. ↩

  6. Conference, N.I.P.S. 2021. Introducing the NeurIPS 2021 Paper Checklist. Medium. ↩

  7. Gundersen, O.E. and Kjensmo, S. 2018. State of the Art: Reproducibility in Artificial Intelligence. Proceedings of the AAAI Conference on Artificial Intelligence. 32, 1 (2018). ↩

  8. Isdahl, R. and Gundersen, O.E. 2019. Out-of-the-Box Reproducibility: A Survey of Machine Learning Platforms. 2019 15th International Conference on [eScience]{.nocase} ([eScience]{.nocase}) (San Diego, CA, USA, 2019), 86--95. ↩

  9. Stodden, V. et al. 2018. AIM: AN ABSTRACTION FOR IMPROVING MACHINE LEARNING PREDICTION. 2018 IEEE Data Science Workshop (DSW) (Lausanne, Switzerland, 2018), 1--5. ↩

  10. Midwinter, M. et al. 2021. Resolution adaptive networks for efficient inference. ↩

  11. Pineau, J. et al. 2020. Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program). arXiv:2003.12206 [cs, stat]. (2020). ↩

  12. Raghupathi, W. et al. 2022. Reproducibility in Computing Research: An Empirical Study. IEEE Access. 10, (2022), 29207--29223. DOI:https://doi.org/10.1109/ACCESS.2022.3158675. ↩

  13. Sadjadi, M. 2017. Arxivscraper. ↩