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Automatic differentiation in machine learning: A survey

Abstract:

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply “autodiff”, is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs. AD is a small but established field with applications in areas including computational fluid dynamics, atmospheric sciences, a...

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Publication status:
Published
Peer review status:
Peer reviewed

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Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0001-9854-8100
Publisher:
Journal of Machine Learning Research
Journal:
Journal of Machine Learning Research More from this journal
Volume:
18
Article number:
153
Publication date:
2018-04-01
Acceptance date:
2017-08-01
EISSN:
1533-7928
ISSN:
1532-4435
Language:
English
Keywords:
Pubs id:
853503
Local pid:
pubs:853503
Deposit date:
2020-06-25

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