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Adaptive neural compilation

Abstract:

This paper proposes an adaptive neural-compilation framework to address the problem of efficient program learning. Traditional code optimisation strategies used in compilers are based on applying pre-specified set of transformations that make the code faster to execute without changing its semantics. In contrast, our work involves adapting programs to make them more efficient while considering correctness only on a target input distribution. Our approach is inspired by the recent works on dif...

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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:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More from this funder
Grant:
EPSRC/MURI EP/N019474/1
EP/M013774/1
Programme Grant Seebibyte EP/M013774/1
More from this funder
Grant:
ERC-2012-AdG 321162-HELIOS
Leverhulme Trust More from this funder
Clarendon Fund More from this funder
Publisher:
Neural Information Processing Systems Foundation Publisher's website
Journal:
NIPS 2016 Journal website
Host title:
NIPS 2016: 29th Annual Conference on Neural Information Processing Systems
Publication date:
2016-10-01
Acceptance date:
2016-05-20
Event location:
Barcelona
ISSN:
1049-5258
Source identifiers:
656040
Pubs id:
pubs:656040
UUID:
uuid:28ab37b9-88f1-48c3-a232-1095c4f894c8
Local pid:
pubs:656040
Deposit date:
2016-11-01

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