论文标题
在高能量物理学中的机器学习应用中的贝叶斯和粒子群算法的比较
Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physics
论文作者
论文摘要
使用机器学习（ML）技术时，用户通常需要选择多种算法特异性参数，称为超参数。在本文中，我们比较了两种算法，粒子群优化（PSO）和贝叶斯优化（BO）的性能，以自主确定这些应用程序在应用中对高能物理（HEP）领域典型的不同ML任务的自主测定。我们对性能的评估包括比较PSO和BO算法有效利用当代HEP实验特征的高度平行计算资源的能力的比较。
When using machine learning (ML) techniques, users typically need to choose a plethora of algorithm-specific parameters, referred to as hyperparameters. In this paper, we compare the performance of two algorithms, particle swarm optimisation (PSO) and Bayesian optimisation (BO), for the autonomous determination of these hyperparameters in applications to different ML tasks typical for the field of high energy physics (HEP). Our evaluation of the performance includes a comparison of the capability of the PSO and BO algorithms to make efficient use of the highly parallel computing resources that are characteristic of contemporary HEP experiments.
