论文标题
资源感知的帕累托最佳自动化机器学习平台
Resource-Aware Pareto-Optimal Automated Machine Learning Platform
论文作者
论文摘要
在这项研究中，我们介绍了一种新颖的平台资源感知汽车（RA-AUTOML），该汽车（RA-AUTOML）使灵活和广义的算法能够构建具有多个目标以及资源和硬件约束的机器学习模型。 RA-AUTOML智能地进行了超参数搜索（HPS）以及神经体系结构搜索（NAS），以构建优化预定目标的模型。 RA-AUTOML是一个多功能的框架，允许用户规定许多资源/硬件约束以及当前问题或业务需求要求的目标。 RA-AUTOML的核心依赖我们的内部搜索引擎算法Moboga，该算法结合了修改的约束意识到的贝叶斯优化和遗传算法来构建帕累托最佳候选者。与最先进的神经网络模型获得的结果相比，我们在CIFAR-10数据集上的实验非常好，同时以模型大小的形式受到资源约束。
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.
