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arXiv 2012.04084math.NThep-thstat.ML
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机器学习算术曲线

Machine-Learning Arithmetic Curves

Yang-Hui He, Kyu-Hwan Lee, Thomas Oliver

AI总结:

本文利用约十万条数据训练标准机器学习模型,对椭圆曲线 BSD 不变量及亏格 2 曲线类似不变量进行分类,准确率超过 0.97,部分任务达 0.998。

摘要:

我们证明,标准机器学习算法经过训练后,可以预测低亏格算术曲线的某些不变量。利用规模约为十万的数据集,我们展示了机器学习在与椭圆曲线的 BSD 不变量(包括其秩和挠子群)以及亏格 2 曲线的类似不变量相关的分类问题中的效用。我们的结果表明,经过训练的机器能够依据这些不变量高效地对曲线进行分类,准确率很高(>0.97)。对于区分挠阶以及识别整点等问题,准确率可达 0.998。

英文摘要:

We show that standard machine-learning algorithms may be trained to predict certain invariants of low genus arithmetic curves. Using datasets of size around one hundred thousand, we demonstrate the utility of machine-learning in classification problems pertaining to the BSD invariants of an elliptic curve (including its rank and torsion subgroup), and the analogous invariants of a genus 2 curve. Our results show that a trained machine can efficiently classify curves according to these invariants with high accuracies (>0.97). For problems such as distinguishing between torsion orders, and the recognition of integral points, the accuracies can reach 0.998.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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