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Using synthetic data to train neural networks is model-based reasoning

Abstract:

We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a neural network using synthetic data can be viewed as learning a proposal distribution generator for approximate inference in the synthetic-data generative model. We demonstrate this connection in a recognition task where we develop a novel Captcha-breaking architecture and train it using synthetic data, demonstratin...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/IJCNN.2017.7966298

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Societies, Other & Subsidiary Companies
Department:
Kellogg College
Oxford college:
Kellogg College
Role:
Author
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Funding agency for:
Le, T
Grant:
DF6700
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Funding agency for:
Baydin, A
Grant:
FA8750-14-2-0006/61160290-111668
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Funding agency for:
Zinkov, R
Grant:
FA8750-14-2-0007
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Funding agency for:
Wood, F
Grant:
FA8750-14-2-0006/61160290-111668
Publisher:
IEEE Publisher's website
Journal:
International Joint Conference on Neural Networks Journal website
Pages:
3514-3521
Host title:
The 30th International Joint Conference on Neural Networks, May 14-19, 2017
Publication date:
2017-07-03
Acceptance date:
2017-02-27
Event location:
Anchorage, Alaska, USA
DOI:
ISSN:
2161-4407
Source identifiers:
684943
ISBN:
9781509061839
Pubs id:
pubs:684943
UUID:
uuid:d1bcfc62-a8f0-4e9a-8301-927a3be56af9
Local pid:
pubs:684943
Deposit date:
2017-03-08

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