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Inference compilation and universal probabilistic programming

Abstract:

We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do “compilation of inference” because our method transforms a denotational specification of an inference problem in the form of a probabilistic program written in a universal programming language into ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
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
Publisher:
Journal of Machine Learning Research Publisher's website
Journal:
AI & Statistics 2017 Journal website
Host title:
AI & Statistics - AISTATS 2017
Publication date:
2017-04-01
Acceptance date:
2017-01-24
Event location:
Lauderdale, Florida, USA
Event start date:
2017-04-20T00:00:00Z
Event end date:
2047-04-22T00:00:00Z
Source identifiers:
684975
Pubs id:
pubs:684975
UUID:
uuid:b6f09ebf-9f66-433b-b71b-3980b577047c
Local pid:
pubs:684975
Deposit date:
2017-03-08

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