Algahtani, Eyad and Kazakov, Dimitar Lubomirov orcid.org/0000-0002-0637-8106 (2018) GPU-Accelerated Hypothesis Cover Set Testing for Learning in Logic. In: CEUR Proceedings of the 28th International Conference on Inductive Logic Programming. CEUR Workshop Proceedings
Abstract
ILP learners are commonly implemented to consider sequentially each training example for each of the hypotheses tested. Computing the cover set of a hypothesis in this way is costly, and introduces a major bottleneck in the learning process. This computation can be implemented more efficiently through the use of data level parallelism. Here we propose a GPU-accelerated approach to this task for propositional logic and for a subset of first order logic. This approach can be used with one’s strategy of choice for the exploration of the hypothesis space. At present, the hypothesis language is limited to logic formulae using unary and binary predicates, such as those covered by certain types of description logic. The approach is tested on a commodity GPU and datasets of up to 200 million training examples, achieving run times of below 30ms per cover set computation.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 07 Sep 2018 15:00 |
Last Modified: | 21 Jan 2025 18:24 |
Status: | Published online |
Publisher: | CEUR Workshop Proceedings |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:135463 |
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