What Do Machine Learning Researchers Mean by "Reproducible"?
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910728221360128 |
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| author | Raff, Edward Benaroch, Michel Samtani, Sagar Farris, Andrew L. |
| author_facet | Raff, Edward Benaroch, Michel Samtani, Sagar Farris, Andrew L. |
| contents | The concern that Artificial Intelligence (AI) and Machine Learning (ML) are entering a "reproducibility crisis" has spurred significant research in the past few years. Yet with each paper, it is often unclear what someone means by "reproducibility". Our work attempts to clarify the scope of "reproducibility" as displayed by the community at large. In doing so, we propose to refine the research to eight general topic areas. In this light, we see that each of these areas contains many works that do not advertise themselves as being about "reproducibility", in part because they go back decades before the matter came to broader attention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03854 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | What Do Machine Learning Researchers Mean by "Reproducible"? Raff, Edward Benaroch, Michel Samtani, Sagar Farris, Andrew L. Machine Learning Artificial Intelligence The concern that Artificial Intelligence (AI) and Machine Learning (ML) are entering a "reproducibility crisis" has spurred significant research in the past few years. Yet with each paper, it is often unclear what someone means by "reproducibility". Our work attempts to clarify the scope of "reproducibility" as displayed by the community at large. In doing so, we propose to refine the research to eight general topic areas. In this light, we see that each of these areas contains many works that do not advertise themselves as being about "reproducibility", in part because they go back decades before the matter came to broader attention. |
| title | What Do Machine Learning Researchers Mean by "Reproducible"? |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.03854 |