{"title":"Recent Trends in Nature Inspired Computation with Applications to Deep Learning","authors":"Vandana Bharti, Bhaskar Biswas, K. K. Shukla","doi":"10.1109/Confluence47617.2020.9057841","DOIUrl":null,"url":null,"abstract":"Nature-inspired computations are commonly recognized optimization techniques that provide optimal solutions to a wide spectrum of computational problems. This paper presents a brief overview of current topics in the field of nature-inspired computation along with their most recent applications in deep learning to identify open challenges concerning the most relevant areas. In addition, we highlight some recent hybridization methods of nature-inspired computation used to optimize the hyper-parameters and architectures of a deep learning framework. Future research as well as prospective deep learning issues are also presented.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Confluence47617.2020.9057841","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Nature-inspired computations are commonly recognized optimization techniques that provide optimal solutions to a wide spectrum of computational problems. This paper presents a brief overview of current topics in the field of nature-inspired computation along with their most recent applications in deep learning to identify open challenges concerning the most relevant areas. In addition, we highlight some recent hybridization methods of nature-inspired computation used to optimize the hyper-parameters and architectures of a deep learning framework. Future research as well as prospective deep learning issues are also presented.