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Neural Analogical Matching. (arXiv:2004.03573v1 [cs.AI])

Date:

(Submitted on 7 Apr 2020)

Abstract: Analogy is core to human cognition. It allows us to solve problems based on
prior experience, it governs the way we conceptualize new information, and it
even influences our visual perception. The importance of analogy to humans has
made it an active area of research in the broader field of artificial
intelligence, resulting in data-efficient models that learn and reason in
human-like ways. While analogy and deep learning have generally been considered
independently of one another, the integration of the two lines of research
seems like a promising step towards more robust and efficient learning
techniques. As part of the first steps towards such an integration, we
introduce the Analogical Matching Network; a neural architecture that learns to
produce analogies between structured, symbolic representations that are largely
consistent with the principles of Structure-Mapping Theory.

Submission history

From: Maxwell Crouse [view email]
[v1]
Tue, 7 Apr 2020 17:50:52 UTC (991 KB)

Source: http://arxiv.org/abs/2004.03573

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