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.

翻译：Quinn等人提出了名为“Kryptonite-N”的挑战数据集。这些数据集旨在反驳机器学习的通用函数逼近论点，打破机器学习能够“逼近任意连续函数”的既有观念。我们的研究驳斥了这一论断，证明通用函数逼近方法仍可成功应用：Kryptonite数据集具有可预测的构造规律，使得具备充分多项式展开和L1正则化的逻辑回归模型能够求解任意维度N的问题。