Towards lighter and faster deep neural networks with parameter pruning

Hubens, Nathan

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Doctoral thesis (Dissertations and theses)

Towards lighter and faster deep neural networks with parameter pruning

Hubens, Nathan

2022

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https://hdl.handle.net/20.500.12907/53625

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Disciplines :

Computer science

Author, co-author :

Hubens, Nathan ; Université de Mons - UMONS > Faculté Polytechnique > Service Information, Signal et Intelligence artificielle

Language :

English

Title :

Towards lighter and faster deep neural networks with parameter pruning

Defense date :

December 2022

Institution :

UMONS - Université de Mons, Belgium
Institut Polytechnique de Paris, France

Degree :

Doctorat en Sciences de l'ingénieur et technologie

Cotutelle degree :

Doctorat en Signal, Image, Automatique et Robotique

Promotor :

Gosselin, Bernard ; Université de Mons - UMONS > Faculté Polytechnique > Service Information, Signal et Intelligence artificielle

Titus Zaharia; Institut Polytechnique de Paris

President :

Moeyaert, Véronique ; Université de Mons - UMONS > Faculté Polytechnique > Service d'Electromagnétisme et Télécommunications

Jury member :

Mancas, Matei ; Université de Mons - UMONS > Faculté Polytechnique > Service Information, Signal et Intelligence artificielle

John Lee; UCL - Université Catholique de Louvain

Bruno Grilhères; Airbus Defence and Space

Marius Preda; Institut Polytechnique de Paris

Research unit :

F105 - Information, Signal et Intelligence artificielle

Research institute :

R450 - Institut NUMEDIART pour les Technologies des Arts Numériques

Available on ORBi UMONS :

since 17 November 2025

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