学习结构化声明规则集--深度离散学习的挑战
Learning Structured Declarative Rule Sets -- A Challenge for Deep Discrete Learning
摘要：
可以说，深度神经网络成功的关键原因是它们能够自主形成输入特征的非线性组合，这些组合可以用于网络的后续层。在归纳规则学习中，类似于这种能力的是学习一个结构化的规则库，其中输入被组合以学习新的辅助概念，然后可以将其用作后续规则的输入。然而，对具有这种能力的规则学习算法的研究仍处于起步阶段，我们认为这是该领域取得实质性进展的关键障碍之一。在这份立场文件中，我们希望引起对这个未解决问题的关注，特别关注谓词发明和多标签规则学习方面的先前工作
英文摘要：
Arguably the key reason for the success of deep neural networks is their ability to autonomously form non-linear combinations of the input features, which can be used in subsequent layers of the network. The analogon to this capability in inductive rule learning is to learn a structured rule base, where the inputs are combined to learn new auxiliary concepts, which can then be used as inputs by subsequent rules. Yet, research on rule learning algorithms that have such capabilities is still in their infancy, which is - we would argue - one of the key impediments to substantial progress in this field. In this position paper, we want to draw attention to this unsolved problem, with a particular focus on previous work in predicate invention and multi-label rule learning
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
