Computation Graphs for AAD and Machine Learning: Part II: Adjoint Differentiation and AAD

Research output: Contribution to journal › Journal article › Research

Standard

Computation Graphs for AAD and Machine Learning : Part II: Adjoint Differentiation and AAD. / Savine, Antoine.

In: Wilmott, No. 105, 2020, p. 32–45.

Research output: Contribution to journal › Journal article › Research

Harvard

Savine, A 2020, 'Computation Graphs for AAD and Machine Learning: Part II: Adjoint Differentiation and AAD', Wilmott, no. 105, pp. 32–45. https://doi.org/10.1002/wilm.10818

APA

Savine, A. (2020). Computation Graphs for AAD and Machine Learning: Part II: Adjoint Differentiation and AAD. Wilmott, (105), 32–45. https://doi.org/10.1002/wilm.10818

Vancouver

Savine A. Computation Graphs for AAD and Machine Learning: Part II: Adjoint Differentiation and AAD. Wilmott. 2020;(105):32–45. https://doi.org/10.1002/wilm.10818

Author

Savine, Antoine. / Computation Graphs for AAD and Machine Learning : Part II: Adjoint Differentiation and AAD. In: Wilmott. 2020 ; No. 105. pp. 32–45.

Bibtex

@article{499ced271e384d1aba30d4e2892fb574,
title = "Computation Graphs for AAD and Machine Learning: Part II: Adjoint Differentiation and AAD",
abstract = "Second in a series of three articles with code, exploring the notion of computation graph, with words, mathematics and code, and application in Machine Learning and finance to compute a vast number of derivative sensitivities with spectacular speed and accuracy.",
author = "Antoine Savine",
year = "2020",
doi = "10.1002/wilm.10818",
language = "English",
pages = "32–45",
journal = "Wilmott",
issn = "1540-6962",
publisher = "Wiley",
number = "105",

}

RIS

TY - JOUR

T1 - Computation Graphs for AAD and Machine Learning

T2 - Part II: Adjoint Differentiation and AAD

AU - Savine, Antoine

PY - 2020

Y1 - 2020

N2 - Second in a series of three articles with code, exploring the notion of computation graph, with words, mathematics and code, and application in Machine Learning and finance to compute a vast number of derivative sensitivities with spectacular speed and accuracy.

AB - Second in a series of three articles with code, exploring the notion of computation graph, with words, mathematics and code, and application in Machine Learning and finance to compute a vast number of derivative sensitivities with spectacular speed and accuracy.

U2 - 10.1002/wilm.10818

DO - 10.1002/wilm.10818

M3 - Journal article

SP - 32

EP - 45

JO - Wilmott

JF - Wilmott

SN - 1540-6962

IS - 105

ER -

ID: 250166510