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    Automatic Differentiation in Machine Learning: a Survey

    Baydin, Atilim Gunes and Pearlmutter, Barak A. and Radul, Alexey Andreyevich and Siskind, Jeffrey Mark (2018) Automatic Differentiation in Machine Learning: a Survey. Journal of Marchine Learning Research, 18. pp. 1-43. ISSN 1532-4435

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    Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply “auto-diff”, 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, and engineering design optimization. Until very recently, the fields of machine learning and AD have largely been unaware of each other and, in some cases, have independently discovered each other’s results. Despite its relevance, general-purpose AD has been missing from the machine learning toolbox, a situation slowly changing with its ongoing adoption under the names “dynamic computational graphs” and “differentiable programming”. We survey the intersection of AD and machine learning, cover applications where AD has direct relevance, and address the main imple- mentation techniques. By precisely defining the main differentiation techniques and their interrelationships, we aim to bring clarity to the usage of the terms “autodiff”, “automatic differentiation”, and “symbolic differentiation” as these are encountered more and more in machine learning settings.

    Item Type: Article
    Additional Information: CC-BY 4.0, see Attribution requirements are provided at
    Keywords: Backpropagation; Differentiable Programming;
    Academic Unit: Faculty of Arts & Humanities > Philosophy
    Item ID: 10227
    Depositing User: Barak Pearlmutter
    Date Deposited: 19 Nov 2018 15:39
    Journal or Publication Title: Journal of Marchine Learning Research
    Publisher: Microtome Publishing
    Refereed: Yes
    Funders: Science Foundation Ireland (SFI), Army Research Laboratory, National Science Foundation, Intelligence Advanced Research Projects Activity (IARPA)
    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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