Resource-Aware Pareto-Optimal Automated Machine Learning Platform

• URL: http://arxiv.org/abs/2011.00073v1
• Date: Fri, 30 Oct 2020 19:37:48 GMT
• Title: Resource-Aware Pareto-Optimal Automated Machine Learning Platform
• Authors: Yao Yang, Andrew Nam, Mohamad M. Nasr-Azadani, Teresa Tung
• Abstract summary: novel platform Resource-Aware AutoML (RA-AutoML) RA-AutoML enables flexible and generalized algorithms to build machine learning models subjected to multiple objectives.
• Score: 1.6746303554275583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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