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Stable ResNet

Abstract:

Deep ResNet architectures have achieved state of the art performance on many tasks. While they solve the problem of gradient vanishing, they might suffer from gradient exploding as the depth becomes large. Moreover, recent results have shown that ResNet might lose expressivity as the depth goes to infinity [Yang and Schoenholz, 2017, Hayou et al., 2019a]. To resolve these issues, we introduce a new class of ResNet architectures, called Stable ResNet, that have the property of stabilizing the ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
Publisher:
Journal of Machine Learning Research
Host title:
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics
Series:
Proceedings of Machine Learning Research
Series number:
130
Pages:
1324-1332
Publication date:
2021-03-29
Acceptance date:
2021-01-22
Event title:
24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021)
Event location:
Virtual event
Event website:
https://aistats.org/aistats2021/
Event start date:
2021-04-13
Event end date:
2021-04-15
ISSN:
2640-3498
Language:
English
Keywords:
Pubs id:
1192441
Local pid:
pubs:1192441
Deposit date:
2022-03-10

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