Paper published in a journal (Scientific congresses, symposiums and conference proceedings)
Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment
HU, Qiang; GUO, Yuejun; CORDY, Maxime et al.
2023In 2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023, p. 56–67
Peer reviewed
 

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Disciplines :
Computer science
Author, co-author :
HU, Qiang ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal
GUO, Yuejun ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust > SerVal > Team Yves LE TRAON
CORDY, Maxime  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal
Xie, Xiaofei
MA, Wei ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust > SerVal > Team Mike PAPADAKIS
PAPADAKIS, Mike ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal
Traon, YvesLe
External co-authors :
yes
Language :
English
Title :
Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment
Publication date :
2023
Event name :
2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023
Event date :
2023
Audience :
International
Journal title :
2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023
Pages :
56–67
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 28 December 2023

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