论文标题
通过混合神经符号模型生成新概念
Generating new concepts with hybrid neuro-symbolic models
论文作者
论文摘要
人类的概念知识支持产生新颖但高度结构化的概念的能力，这种概念知识的形式对认知科学家引起了极大的兴趣。一种传统强调了结构化的知识，将概念视为嵌入直观理论或在复杂的象征知识结构中组织的概念。第二种传统强调了统计知识，将概念知识视为训练神经网络和其他统计模型所捕获的丰富相关结构的出现。在本文中，我们通过一种新的神经符号模型来探讨这两种传统的综合，用于产生新的概念。使用简单的视觉概念作为测试台，我们将神经网络和象征性概率程序汇总在一起，以学习新型手写字符的生成模型。通过更通用的神经网络体系结构探索了两个替代模型。我们比较了这三个模型中的每一个，以便它们在持有的角色类别和制作质量上的可能性，发现我们的混合动力模型可以学习最令人信服的代表，并从培训观察中进一步概括。
Human conceptual knowledge supports the ability to generate novel yet highly structured concepts, and the form of this conceptual knowledge is of great interest to cognitive scientists. One tradition has emphasized structured knowledge, viewing concepts as embedded in intuitive theories or organized in complex symbolic knowledge structures. A second tradition has emphasized statistical knowledge, viewing conceptual knowledge as an emerging from the rich correlational structure captured by training neural networks and other statistical models. In this paper, we explore a synthesis of these two traditions through a novel neuro-symbolic model for generating new concepts. Using simple visual concepts as a testbed, we bring together neural networks and symbolic probabilistic programs to learn a generative model of novel handwritten characters. Two alternative models are explored with more generic neural network architectures. We compare each of these three models for their likelihoods on held-out character classes and for the quality of their productions, finding that our hybrid model learns the most convincing representation and generalizes further from the training observations.
