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arXiv.org e-Print Archive
Preprint . 2018
Data sources: arXiv.org e-Print Archive
https://dx.doi.org/10.48550/ar...
Article . 2018
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Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2018Embargo end date: 01 Jan 2018Publisher:arXiv
Authors: van Merriënboer, Bart; Moldovan, Dan; Wiltschko, Alexander B;

doi: 10.48550/arxiv.1809.09569

arXiv: 1809.09569

Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming

Abstract

The need to efficiently calculate first- and higher-order derivatives of increasingly complex models expressed in Python has stressed or exceeded the capabilities of available tools. In this work, we explore techniques from the field of automatic differentiation (AD) that can give researchers expressive power, performance and strong usability. These include source-code transformation (SCT), flexible gradient surgery, efficient in-place array operations, higher-order derivatives as well as mixing of forward and reverse mode AD. We implement and demonstrate these ideas in the Tangent software library for Python, the first AD framework for a dynamic language that uses SCT.

Keywords

Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Software Engineering, Statistics - Machine Learning, Machine Learning (stat.ML), Machine Learning (cs.LG)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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