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- W4288023218 abstract "Most program induction approaches require predefined, often hand-engineered, background knowledge (BK). To overcome this limitation, we explore methods to automatically acquire BK through multi-task learning. In this approach, a learner adds learned programs to its BK so that they can be reused to help learn other programs. To improve learning performance, we explore the idea of forgetting, where a learner can additionally remove programs from its BK. We consider forgetting in an inductive logic programming (ILP) setting. We show that forgetting can significantly reduce both the size of the hypothesis space and the sample complexity of an ILP learner. We introduce Forgetgol, a multi-task ILP learner which supports forgetting. We experimentally compare Forgetgol against approaches that either remember or forget everything. Our experimental results show that Forgetgol outperforms the alternative approaches when learning from over 10,000 tasks." @default.
- W4288023218 created "2022-07-26" @default.
- W4288023218 creator A5008794311 @default.
- W4288023218 date "2019-11-15" @default.
- W4288023218 modified "2023-09-28" @default.
- W4288023218 title "Forgetting to learn logic programs" @default.
- W4288023218 doi "https://doi.org/10.48550/arxiv.1911.06643" @default.
- W4288023218 hasPublicationYear "2019" @default.
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