Baydin, Atilim Gunes and Pearlmutter, Barak A. and Radul, Alexey Andreyevich and Siskind, Jeffrey Mark (2015) Automatic differentiation in machine learning: a survey. Working Paper. arXiv.

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Abstract

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD) is a technique for calculating derivatives of numeric functions expressed as computer programs efficiently and accurately, used in fields such as computational fluid dynamics, nuclear engineering, and atmospheric sciences. Despite its advantages and use in other fields, machine learning practitioners have been little influenced by AD and make scant use of available tools. We survey the intersection of AD and machine learning, cover applications where AD has the potential to make a big impact, and report on some recent developments in the adoption of this technique. We aim to dispel some misconceptions that we contend have impeded the use of AD within the machine learning community.

Item Type:	Monograph (Working Paper)
Additional Information:	Cite as: arXiv:1502.05767 [cs.SC]
Keywords:	Optimization; Gradient methods; Backpropagation;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	8145
Depositing User:	Barak Pearlmutter
Date Deposited:	10 Apr 2017 12:20
Publisher:	arXiv
Funders:	Science Foundation Ireland (SFI)
URI:	https://arxiv.org
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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