FeatureNET: Diversity-driven Generation of Deep Learning Models

Reference : FeatureNET: Diversity-driven Generation of Deep Learning Models


	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/44547

Title :	FeatureNET: Diversity-driven Generation of Deep Learning Models
Language :	English
Author, co-author :	Ghamizi, Salah [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
	Cordy, Maxime [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Papadakis, Mike [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
	Le Traon, Yves [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	2020
Main document title :	International Conference on Software Engineering (ICSE)
Peer reviewed :	Yes
Audience :	International
Event name :	42nd International Conference on Software Engineering
Event date :	from 6-7-2020 to 11-7-2020
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Security Design and Validation Research Group (SerVal)
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	CODEMATES
Permalink :	http://hdl.handle.net/10993/44547
FnR project :	FnR ; FNR11686509 > Michail Papadakis > CODEMATES > COntinuous DEvelopment with Mutation Analysis and TESting > 01/09/2018 > 31/08/2021 > 2017

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	File	Commentary	Version	Size	Access
Open access	FeatureNET_final_Camera_Ready (4).pdf		Author preprint	859.08 kB	View/Open

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