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A Functional Perspective on Machine Learning via Programmable Induction and Abduction

Cheung, S; Darvariu, V; Ghica, DR; Muroya, K; Rowe, RNS; (2018) A Functional Perspective on Machine Learning via Programmable Induction and Abduction. In: Gallagher, JP and Sulzmann, M, (eds.) Functional and Logic Programming: 14th International Symposium, FLOPS 2018, Nagoya, Japan, May 9–11, 2018, Proceedings. (pp. pp. 84-98). Springer: Cham, Switzerland. Green open access

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Abstract

We present a programming language for machine learning based on the concepts of ‘induction’ and ‘abduction’ as encountered in Peirce’s logic of science. We consider the desirable features such a language must have, and we identify the ‘abductive decoupling’ of parameters as a key general enabler of these features. Both an idealised abductive calculus and its implementation as a PPX extension of OCaml are presented, along with several simple examples.

Type: Proceedings paper
Title: A Functional Perspective on Machine Learning via Programmable Induction and Abduction
Event: 14th International Symposium on Functional and Logic Programming (FLOPS), 9–11 May 2018, Nagoya, Japan
Location: Nagoya, JAPAN
Dates: 09 May 2018 - 11 May 2018
ISBN-13: 978-3-319-90685-0
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-90686-7_6
Publisher version: https://doi.org/10.1007/978-3-319-90686-7_6
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10068974
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