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Automatic Differentiation in Prolog

Published online by Cambridge University Press:  06 July 2023

TOM SCHRIJVERS Open the ORCID record for TOM SCHRIJVERS [Opens in a new window] ,
BIRTHE VAN DEN BERG  and
FABRIZIO RIGUZZI Open the ORCID record for FABRIZIO RIGUZZI [Opens in a new window]
TOM SCHRIJVERS
Affiliation:
KU Leuven, Leuven, Belgium (e-mails: tom.schrijvers@kuleuven.be, birthe.vandenberg@kuleuven.be)
BIRTHE VAN DEN BERG
Affiliation:
KU Leuven, Leuven, Belgium (e-mails: tom.schrijvers@kuleuven.be, birthe.vandenberg@kuleuven.be)
FABRIZIO RIGUZZI
Affiliation:
Università degli Studi di Ferrara, Ferrara, Italy (e-mail: fabrizio.riguzzi@unife.it)
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Abstract

Automatic differentiation (AD) is a range of algorithms to compute the numeric value of a function’s (partial) derivative, where the function is typically given as a computer program or abstract syntax tree. AD has become immensely popular as part of many learning algorithms, notably for neural networks. This paper uses Prolog to systematically derive gradient-based forward- and reverse-mode AD variants from a simple executable specification: evaluation of the symbolic derivative. Along the way we demonstrate that several Prolog features (DCGs, co-routines) contribute to the succinct formulation of the algorithm. We also discuss two applications in probabilistic programming that are enabled by our Prolog algorithms. The first is parameter learning for the Sum-Product Loop Language and the second consists of both parameter learning and variational inference for probabilistic logic programming.

Keywords

Prologautomatic differentiationprobabilistic programming

Information

Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 4: 2023 International Conference on Logic Programming , July 2023 , pp. 900 - 917
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Copyright
© The Author(s), 2023. Published by Cambridge University Press

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Footnotes

*

This project is partly funded by the Flemish Fund for Scientific Research (FWO).

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