论文标题
神经类似匹配
Neural Analogical Matching
论文作者
论文摘要
类比是人类认知的核心。它使我们能够根据先前的经验解决问题，它控制着我们概念化新信息的方式，甚至影响了我们的视觉感知。类比对人类的重要性使其成为更广泛的人工智能领域的积极研究领域，从而产生了以人类方式学习和理性的数据效率模型。尽管通常对彼此独立研究了类比和深度学习的认知观点，但两条研究的整合是朝着更强大，更有效的学习技术迈出的有希望的一步。作为对这种整合的越来越多的研究的一部分，我们介绍了类似匹配网络：一种学会在结构化的，符号表示之间产生类比的神经结构，这些表示与结构映射理论的原理很大程度上一致。
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 cognitive perspectives of analogy and deep learning have generally been studied independently of one another, the integration of the two lines of research is a promising step towards more robust and efficient learning techniques. As part of a growing body of research on 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.
