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arXiv · 2412.20588

Kryptonite-N: Machine Learning Strikes Back

Albus Li·Nathan Bailey·Will Sumerfield·Kira Kim
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Abstract

Quinn et al propose challenge datasets in their work called ``Kryptonite-N". These datasets aim to counter the universal function approximation argument of machine learning, breaking the notation that machine learning can ``approximate any continuous function" \cite{original_paper}. Our work refutes this claim and shows that universal function approximations can be applied successfully; the Kryptonite datasets are constructed predictably, allowing logistic regression with sufficient polynomial expansion and L1 regularization to solve for any dimension N.

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Albus Li, Nathan Bailey, Will Sumerfield, Kira Kim. 2024-12-29. Kryptonite-N: Machine Learning Strikes Back. https://arxiv.org/abs/2412.20588

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