• Citer

Self-Optimisation of Dense Neural Network Architectures: An Incremental Approach

par García-Díaz, Antonio ;Bersini, Hugues
Référence 2020 International Joint Conference on Neural Networks (IJCNN)(19-24 July 2020: Glasgow, UK), 2020 International Joint Conference on Neural Networks (IJCNN), 2020 Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE)
Publication Publié, 2020-09-28

Publication dans des actes

• Accès en ligne
• Détails
• Contenu
• Statistiques

Titre:	Self-Optimisation of Dense Neural Network Architectures: An Incremental Approach
Auteur:	García-Díaz, Antonio; Bersini, Hugues
Informations sur la publication:	2020 International Joint Conference on Neural Networks (IJCNN)(19-24 July 2020: Glasgow, UK), 2020 International Joint Conference on Neural Networks (IJCNN), 2020 Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE)
Statut de publication:	Publié, 2020-09-28
Sujet CREF:	Intelligence artificielle
	Informatique mathématique
	Informatique générale
Mots-clés:	Neurons, Network topology, Computer architecture, Heuristic algorithms, Training
	convolutional neural nets, image classification, neural net architecture, optimisation
	convolutional neural networks, DenseNet-BC architectures layer, image classification datasets, CIFAR-10, self-optimisation, dense neural network architectures, incremental approach, self-structuring algorithm
	neural network, architecture, topology, optimization, self-structuring, DenseNet, EMANN, connection strength
Langue:	Anglais
Identificateurs:	urn:isbn:978-1-7281-6926-2
	info:doi/10.1109/IJCNN48605.2020.9207416