Automatic Differentiation in Prolog-Reference-Cited by-同舟云学术

Automatic Differentiation in Prolog

Published:2023-07 Issue:4 Volume:23 Page:900-917
ISSN:1471-0684
Container-title:Theory and Practice of Logic Programming
language:en
Short-container-title:Theory and Practice of Logic Programming

Author:

SCHRIJVERS TOM^ORCID,VAN DEN BERG BIRTHE,RIGUZZI FABRIZIO^ORCID

Abstract

AbstractAutomatic 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.

Publisher

Cambridge University Press (CUP)

Subject

Artificial Intelligence,Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software

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