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 studied 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.

中文翻译：

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神经类比匹配

类比是人类认知的核心。它允许我们根据先前的经验解决问题，它控制着我们对新信息的概念化方式，甚至影响我们的视觉感知。类比对人类的重要性使其成为更广泛的人工智能领域的一个活跃研究领域，从而产生了以类似人类的方式学习和推理的数据高效模型。虽然类比和深度学习通常是相互独立研究的，但将这两条研究线结合起来似乎是朝着更强大和更有效的学习技术迈出的有希望的一步。作为实现这种集成的第一步的一部分，我们介绍了类比匹配网络：一种学习在结构化、