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Thesis

Toward efficient deep learning with sparse neural networks

Abstract:

Despite the tremendous success that deep learning has achieved in recent years, it remains challenging to deal with the excessive computational and memory cost involved in executing deep learning based applications. To address the challenge, this thesis focuses on studying sparse neural networks, particularly around their construction, initialization, and large-scale training aspects, as an attempt to take a step toward efficient deep learning.

Firstly, this thesis addresses the pr...

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Division:
MPLS
Department:
Engineering Science
Role:
Author

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Role:
Supervisor
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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