Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks. Here the authors propose a method to design neural networks as sparse scale-free networks, which leads to a reduction in computational time required for training and inference.

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Autores principales:	Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H. Nguyen, Madeleine Gibescu, Antonio Liotta
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/50ee9604a82c41788aeb102570ad016f
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spelling	oai:doaj.org-article:50ee9604a82c41788aeb102570ad016f2021-12-02T14:40:54ZScalable training of artificial neural networks with adaptive sparse connectivity inspired by network science10.1038/s41467-018-04316-32041-1723https://doaj.org/article/50ee9604a82c41788aeb102570ad016f2018-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-04316-3https://doaj.org/toc/2041-1723Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks. Here the authors propose a method to design neural networks as sparse scale-free networks, which leads to a reduction in computational time required for training and inference.Decebal Constantin MocanuElena MocanuPeter StonePhuong H. NguyenMadeleine GibescuAntonio LiottaNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-12 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Decebal Constantin Mocanu Elena Mocanu Peter Stone Phuong H. Nguyen Madeleine Gibescu Antonio Liotta Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
description	Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks. Here the authors propose a method to design neural networks as sparse scale-free networks, which leads to a reduction in computational time required for training and inference.
format	article
author	Decebal Constantin Mocanu Elena Mocanu Peter Stone Phuong H. Nguyen Madeleine Gibescu Antonio Liotta
author_facet	Decebal Constantin Mocanu Elena Mocanu Peter Stone Phuong H. Nguyen Madeleine Gibescu Antonio Liotta
author_sort	Decebal Constantin Mocanu
title	Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
title_short	Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
title_full	Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
title_fullStr	Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
title_full_unstemmed	Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
title_sort	scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/50ee9604a82c41788aeb102570ad016f
work_keys_str_mv	AT decebalconstantinmocanu scalabletrainingofartificialneuralnetworkswithadaptivesparseconnectivityinspiredbynetworkscience AT elenamocanu scalabletrainingofartificialneuralnetworkswithadaptivesparseconnectivityinspiredbynetworkscience AT peterstone scalabletrainingofartificialneuralnetworkswithadaptivesparseconnectivityinspiredbynetworkscience AT phuonghnguyen scalabletrainingofartificialneuralnetworkswithadaptivesparseconnectivityinspiredbynetworkscience AT madeleinegibescu scalabletrainingofartificialneuralnetworkswithadaptivesparseconnectivityinspiredbynetworkscience AT antonioliotta scalabletrainingofartificialneuralnetworkswithadaptivesparseconnectivityinspiredbynetworkscience
_version_	1718390109790797824

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

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