Summary of “forgetting” in Machine Learning and Beyond: a Survey, by Alyssa Shuang Sha et al.
“Forgetting” in Machine Learning and Beyond: A Survey
by Alyssa Shuang Sha, Bernardo Pereira Nunes, Armin Haller
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The medium-difficulty summary: This survey on forgetting in machine learning explores how this concept can be beneficial for enhancing the learning process and preventing overfitting. The study draws insights from neuroscientific research that views forgetting as an adaptive function, rather than a defect. By focusing on the benefits of forgetting, the paper highlights its applications across various machine learning subfields, including those related to model performance and data privacy. Additionally, the authors discuss current challenges, future directions, and ethical considerations regarding the integration of forgetting mechanisms into machine learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The low-difficulty summary: This study looks at how “forgetting” in machine learning can actually help improve how well models work. Instead of thinking of it as a bad thing, researchers are realizing that forgetting can be an important part of making models better. The paper explores how this idea can be used to make machine learning more effective and protect our data better. |
Keywords
» Artificial intelligence » Machine learning » Overfitting
