贝叶斯算法与粒子群算法在高能物理机器学习应用中的超参数优化比较
Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physics
AI总结：
本文比较粒子群优化与贝叶斯优化在高能物理机器学习任务中自主调优超参数的性能，并评估其对并行计算资源的利用效率。
摘要：
在使用机器学习（ML）技术时，用户通常需要选择大量算法特定的参数，即超参数。本文比较了两种算法——粒子群优化（PSO）和贝叶斯优化（BO）——在高能物理（HEP）领域典型的多种机器学习任务中自主确定这些超参数的性能。我们的性能评估包括比较PSO和BO算法高效利用高度并行计算资源的能力，这些资源是当代HEP实验的特征。
英文摘要：
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.
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