论文标题
逻辑上的深度学习
Deep Learning with Logical Constraints
论文作者
论文摘要
近年来，人们对利用逻辑指定的背景知识的利益越来越兴趣，以获取具有更好性能的神经模型（i），（ii）能够从较少的数据中学习，并且/或（iii）保证符合背景知识本身，例如，例如，以安全性为关键应用。在这项调查中，我们根据（i）他们用来表达背景知识的逻辑语言和（ii）实现目标的逻辑语言对这些作品进行了重新归类。
In recent years, there has been an increasing interest in exploiting logically specified background knowledge in order to obtain neural models (i) with a better performance, (ii) able to learn from less data, and/or (iii) guaranteed to be compliant with the background knowledge itself, e.g., for safety-critical applications. In this survey, we retrace such works and categorize them based on (i) the logical language that they use to express the background knowledge and (ii) the goals that they achieve.
