Deep Learning with Logical Constraints
Eleonora Giunchiglia, Mihaela Cătălina Stoian, Thomas Lukasiewicz
2022Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence39 citationsDOIOpen Access PDF
Abstract
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.
Topics & Concepts
Computer scienceCategorizationArtificial intelligenceDeep learningLogical consequenceMachine learningNatural language processingAdversarial Robustness in Machine LearningTopic ModelingExplainable Artificial Intelligence (XAI)