Summary of Lessons Learned: Reproducibility, Replicability, and When to Stop, by Milton S. Gomez et al.
Lessons Learned: Reproducibility, Replicability, and When to Stop
by Milton S. Gomez, Tom Beucler
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Atmospheric and Oceanic Physics (physics.ao-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As machine learning educators, we aim to reproduce and replicate studies in our own research while drawing from existing knowledge. A two-dimensional framework is presented, which incorporates three key aspects: dataset, metrics, and model itself. By mapping our research trajectories on this plane, we can better inform claims made using our work. This framework is useful for researchers, particularly early career researchers, to incorporate prior work into their own studies and make informed claims. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Have you ever wondered how scientists ensure the accuracy of their findings? A new framework helps researchers reproduce and replicate each other’s work! It’s like a roadmap that shows how different parts of a study fit together. By using this map, scientists can be more confident in their results and even build on what others have discovered before. |
Keywords
* Artificial intelligence * Machine learning
