Fast-DENSER: Fast Deep Evolutionary Network Structured Representation

Detalhes bibliográficos
Autor(a) principal: Assunção, Filipe
Data de Publicação: 2021
Outros Autores: Lourenço, Nuno, Ribeiro, Bernardete, Machado, Penousal
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/100856
https://doi.org/10.1016/j.softx.2021.100694
Resumo: This paper introduces a grammar-based general purpose framework for the automatic search and deployment of potentially Deep Artificial Neural Networks (DANNs). The approach is known as Fast Deep Evolutionary Network Structured Representation (Fast-DENSER) and is capable of simultaneously optimising the topology, learning strategy and any other required hyper-parameters (e.g., data pre-processing or augmentation). Fast-DENSER has been successfully applied to numerous object recognition tasks, with the generation of Convolutional Neural Networks (CNNs). The code is developed and tested in Python3, and made available as a library. A simple and easy to follow example is described for the automatic search of CNNs for the Fashion-MNIST benchmark
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spelling Fast-DENSER: Fast Deep Evolutionary Network Structured RepresentationArtificial Neural NetworksAutomated machine learningNeuroEvolutionThis paper introduces a grammar-based general purpose framework for the automatic search and deployment of potentially Deep Artificial Neural Networks (DANNs). The approach is known as Fast Deep Evolutionary Network Structured Representation (Fast-DENSER) and is capable of simultaneously optimising the topology, learning strategy and any other required hyper-parameters (e.g., data pre-processing or augmentation). Fast-DENSER has been successfully applied to numerous object recognition tasks, with the generation of Convolutional Neural Networks (CNNs). The code is developed and tested in Python3, and made available as a library. A simple and easy to follow example is described for the automatic search of CNNs for the Fashion-MNIST benchmark2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/100856http://hdl.handle.net/10316/100856https://doi.org/10.1016/j.softx.2021.100694eng23527110Assunção, FilipeLourenço, NunoRibeiro, BernardeteMachado, Penousalinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-07-15T20:35:02Zoai:estudogeral.uc.pt:10316/100856Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:18:09.362943Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Fast-DENSER: Fast Deep Evolutionary Network Structured Representation
title Fast-DENSER: Fast Deep Evolutionary Network Structured Representation
spellingShingle Fast-DENSER: Fast Deep Evolutionary Network Structured Representation
Assunção, Filipe
Artificial Neural Networks
Automated machine learning
NeuroEvolution
title_short Fast-DENSER: Fast Deep Evolutionary Network Structured Representation
title_full Fast-DENSER: Fast Deep Evolutionary Network Structured Representation
title_fullStr Fast-DENSER: Fast Deep Evolutionary Network Structured Representation
title_full_unstemmed Fast-DENSER: Fast Deep Evolutionary Network Structured Representation
title_sort Fast-DENSER: Fast Deep Evolutionary Network Structured Representation
author Assunção, Filipe
author_facet Assunção, Filipe
Lourenço, Nuno
Ribeiro, Bernardete
Machado, Penousal
author_role author
author2 Lourenço, Nuno
Ribeiro, Bernardete
Machado, Penousal
author2_role author
author
author
dc.contributor.author.fl_str_mv Assunção, Filipe
Lourenço, Nuno
Ribeiro, Bernardete
Machado, Penousal
dc.subject.por.fl_str_mv Artificial Neural Networks
Automated machine learning
NeuroEvolution
topic Artificial Neural Networks
Automated machine learning
NeuroEvolution
description This paper introduces a grammar-based general purpose framework for the automatic search and deployment of potentially Deep Artificial Neural Networks (DANNs). The approach is known as Fast Deep Evolutionary Network Structured Representation (Fast-DENSER) and is capable of simultaneously optimising the topology, learning strategy and any other required hyper-parameters (e.g., data pre-processing or augmentation). Fast-DENSER has been successfully applied to numerous object recognition tasks, with the generation of Convolutional Neural Networks (CNNs). The code is developed and tested in Python3, and made available as a library. A simple and easy to follow example is described for the automatic search of CNNs for the Fashion-MNIST benchmark
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/100856
http://hdl.handle.net/10316/100856
https://doi.org/10.1016/j.softx.2021.100694
url http://hdl.handle.net/10316/100856
https://doi.org/10.1016/j.softx.2021.100694
dc.language.iso.fl_str_mv eng
language eng
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repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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