Summary of Kryptonite-n: Machine Learning Strikes Back, by Albus Li et al.
Kryptonite-N: Machine Learning Strikes Back
by Albus Li, Nathan Bailey, Will Sumerfield, Kira Kim
First submitted to arxiv on: 29 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed challenge datasets in “Kryptonite-N” aim to counter the universal function approximation argument of machine learning. The authors refuted this claim by showing that universal function approximations can be applied successfully, using logistic regression with sufficient polynomial expansion and L1 regularization to solve for any dimension N. This work challenges the notion that machine learning can approximate any continuous function. Specifically, the Kryptonite datasets are constructed predictably, allowing logistic regression models to solve complex problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning researchers have long believed that their models could approximate any continuous function. But what if they’re wrong? Quinn et al challenge this idea with “Kryptonite-N”, a set of special datasets designed to test the limits of machine learning. They show that even simple models like logistic regression can be used to solve complex problems, as long as they have enough power and regularization. This breakthrough challenges our understanding of what machine learning can do. |
Keywords
» Artificial intelligence » Logistic regression » Machine learning » Regularization
