论文标题
机器学习输出到统计标准的简单指南
A simple guide from Machine Learning outputs to statistical criteria
论文作者
论文摘要
在本文中，我们提出了将机器学习培训输出纳入统计意义的研究的方法。我们使用CNN和DNN输出以及基于VAE的无监督学习中描述了这些方法。作为用例，我们考虑了经常使用机器学习的两种物理情况：高-P_T $ HADRONIC活动，并与大型矢量玻色子相连的Higgs增强了Higgs。
In this paper we propose ways to incorporate Machine Learning training outputs into a study of statistical significance. We describe these methods in supervised classification tasks using a CNN and a DNN output, and unsupervised learning based on a VAE. As use cases, we consider two physical situations where Machine Learning are often used: high-$p_T$ hadronic activity, and boosted Higgs in association with a massive vector boson.
