URL details: anselmrothe.github.io/publication/2018cogsci_zendo/
URL title:
Grounding compositional hypothesis generation in specific instances | Anselm Rothe
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Abstract A number of recent computational models treat concept learning as a form of probabilistic rule induction in a space of language-like, compositional concepts. Inference in such models frequently requires repeatedly sampling from a (infinite) distribution over possible concept rules and comparing their relative likelihood in light of current data or evidence. However, we argue that most existing algorithms for top-down sampling are inefficient and cognitively implausible accounts of human hypothesis
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