{"type":"ScholarlyArticle","title":"Deep Learning with Logical Constraints","authors":[{"name":"Eleonora Giunchiglia","slug":"eleonora-giunchiglia","given":"Eleonora","family":"Giunchiglia","affiliation":"Department of Computer Science , University of Oxford , UK"},{"name":"Mihaela Catalina Stoian","slug":"mihaela-catalina-stoian","given":"Mihaela Catalina","family":"Stoian","affiliation":"Department of Computer Science , University of Oxford , UK"},{"name":"Thomas Lukasiewicz","slug":"thomas-lukasiewicz","given":"Thomas","family":"Lukasiewicz","affiliation":"Institute of Logic and Computation , TU Wien , Austria; Department of Computer Science , University of Oxford , UK"}],"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.","doi":"10.24963/ijcai.2022/767","url":"https://ijcai.org/proceedings/2022/767","pdf":"https://ijcai.org/proceedings/2022/0767.pdf","markdown":"https://ijcai.org/proceedings/2022/767.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/767","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Survey Track","track":"Survey Track","number":767,"pages":{"first":5478,"last":5485},"keywords":["Survey Track: Machine Learning","Survey Track: Knowledge Representation and Reasoning","Survey Track: -"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987283","length_sec":80,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985426","length_sec":652,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Badreddine et al., 2022] Samy Badreddine, Artur d'Avila Garcez, Luciano Serafini, and Michael Spranger. 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