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Rational neural networks

Abstract:

We consider neural networks with rational activation functions. The choice of the nonlinear activation function in deep learning architectures is crucial and heavily impacts the performance of a neural network. We establish optimal bounds in terms of network complexity and prove that rational neural networks approximate smooth functions more efficiently than ReLU networks with exponentially smaller depth. The flexibility and smoothness of rational activation functions make them an attractive ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publication website:
https://proceedings.neurips.cc/paper/2020

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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Oxford college:
Christ Church
Role:
Author
ORCID:
0000-0001-7911-1501
Publisher:
Conference on Neural Information Processing Systems Publisher's website
Host title:
Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
Publication date:
2020-12-12
Acceptance date:
2020-09-25
Event title:
Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
Event location:
Online
Event website:
https://neurips.cc/Conferences/2020
Event start date:
2020-12-06T00:00:00Z
Event end date:
2020-12-12T00:00:00Z
Language:
English
Keywords:
Subtype:
Presentation
Pubs id:
1146199
Local pid:
pubs:1146199
Deposit date:
2020-11-20

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