标题： 资源感知的帕累托最优自动化机器学习平台 显示英文标题

标题： Resource-Aware Pareto-Optimal Automated Machine Learning Platform

摘要： 在本研究中，我们引入了一个新的平台资源感知自动机器学习（RA-AutoML），它能够灵活且通用的算法来构建受多个目标以及资源和硬件约束的机器学习模型。RA-AutoML智能地进行超参数搜索（HPS）以及神经网络架构搜索（NAS）以构建优化预定义目标的模型。RA-AutoML是一个多功能框架，允许用户指定许多资源/硬件约束，以及当前问题或业务需求所要求的目标。其核心，RA-AutoML依赖于我们内部的搜索引擎算法MOBOGA，该算法结合了改进的约束感知贝叶斯优化和遗传算法以构建帕累托最优候选者。我们在CIFAR-10数据集上的实验显示，与通过最先进的神经网络模型获得的结果相比，具有非常好的准确性，同时受到模型大小形式的资源约束。 显示英文摘要

摘要： In this study, we introduce a novel platform Resource-Aware AutoML (RA-AutoML) which enables flexible and generalized algorithms to build machine learning models subjected to multiple objectives, as well as resource and hard-ware constraints. RA-AutoML intelligently conducts Hyper-Parameter Search(HPS) as well as Neural Architecture Search (NAS) to build models optimizing predefined objectives. RA-AutoML is a versatile framework that allows user to prescribe many resource/hardware constraints along with objectives demanded by the problem at hand or business requirements. At its core, RA-AutoML relies on our in-house search-engine algorithm,MOBOGA, which combines a modified constraint-aware Bayesian Optimization and Genetic Algorithm to construct Pareto optimal candidates. Our experiments on CIFAR-10 dataset shows very good accuracy compared to results obtained by state-of-art neural network models, while subjected to resource constraints in the form of model size.