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Article (Scientific journals)
Training very deep neural networks: Rethinking the role of skip connections
OYEDOTUN, Oyebade; AL ISMAEIL, Kassem; AOUADA, Djamila
2021 • In Neurocomputing
Peer Reviewed verified by ORBi
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https://hdl.handle.net/10993/47494
DOI
10.1016/j.neucom.2021.02.004
 

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Keywords :
Deep neural network; Residual learning; Skip connection; Optimization
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Disciplines :
Computer science
Author, co-author :
OYEDOTUN, Oyebade ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
AL ISMAEIL, Kassem ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
AOUADA, Djamila  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
External co-authors :
no
Language :
English
Title :
Training very deep neural networks: Rethinking the role of skip connections
Publication date :
21 June 2021
Journal title :
Neurocomputing
ISSN :
0925-2312
eISSN :
1872-8286
Publisher :
Elsevier, Amsterdam, Netherlands
Peer reviewed :
Peer Reviewed verified by ORBi
FnR Project :
FNR11295431 - Automatic Feature Selection For Visual Recognition, 2016 (01/02/2017-31/01/2021) - Oyebade Oyedotun
Funders :
FNR - Fonds National de la Recherche
Available on ORBilu :
since 20 June 2021

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