论文标题
机器学习Dessins d'Enfant：通过模块化和Seiberg-Witten曲线进行探索
Machine-Learning Dessins d'Enfants: Explorations via Modular and Seiberg-Witten Curves
论文作者
论文摘要
我们将机器学习应用于Dessins d'Enfants的研究。具体而言，我们研究了一类Dessins，该类别位于模块化亚组，Seiberg-witten曲线和极端椭圆形K3表面的研究中。具有简单结构和标准激活功能的深层馈送神经网络，而没有先验的基础数学知识，并将其强加于理性的扩展学位分类，这是一个困难的问题。分类达到0.92的精度，标准误差相对较快。还列出了具有有理系数的人的Seiberg-inten曲线。
We apply machine-learning to the study of dessins d'enfants. Specifically, we investigate a class of dessins which reside at the intersection of the investigations of modular subgroups, Seiberg-Witten curves and extremal elliptic K3 surfaces. A deep feed-forward neural network with simple structure and standard activation functions without prior knowledge of the underlying mathematics is established and imposed onto the classification of extension degree over the rationals, known to be a difficult problem. The classifications reached 0.92 accuracy with 0.03 standard error relatively quickly. The Seiberg-Witten curves for those with rational coefficients are also tabulated.
