{"type":"ScholarlyArticle","title":"Scalable Coupling of Deep Learning with Logical Reasoning","authors":[{"name":"Marianne Defresne","slug":"marianne-defresne","given":"Marianne","family":"Defresne","affiliation":"Université Fédérale de Toulouse , ANITI , INRAE , UR 875 , 31326 Toulouse , France; TBI , Université de Toulouse , CNRS , INRAE , INSA , ANITI , 31077 Toulouse , France"},{"name":"Sophie Barbe","slug":"sophie-barbe","given":"Sophie","family":"Barbe","affiliation":"TBI , Université de Toulouse , CNRS , INRAE , INSA , ANITI , 31077 Toulouse , France"},{"name":"Thomas Schiex","slug":"thomas-schiex","given":"Thomas","family":"Schiex","affiliation":"Université Fédérale de Toulouse , ANITI , INRAE , UR 875 , 31326 Toulouse , France"}],"abstract":"In the ongoing quest for hybridizing discrete reasoning with neural nets, there is an increasing interest in neural architectures that can learn how to solve discrete reasoning or optimization problems from natural inputs. In this paper, we introduce a scalable neural architecture and loss function dedicated to learning the constraints and criteria of NP-hard reasoning problems expressed as discrete Graphical Models. We empirically show our loss function is able to efficiently learn how to solve NP-hard reasoning problems from natural inputs as the symbolic, visual or many-solutions Sudoku problems as well as the energy optimization formulation of the protein design problem, providing data efficiency, interpretability, and a posteriori control over predictions.","doi":"10.24963/ijcai.2023/402","url":"https://ijcai.org/proceedings/2023/402","pdf":"https://ijcai.org/proceedings/2023/0402.pdf","markdown":"https://ijcai.org/proceedings/2023/402.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/402","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":402,"pages":{"first":3615,"last":3623},"keywords":["Machine Learning: ML: Neuro-symbolic methods","Constraint Satisfaction and Optimization: CSO: Constraint learning and acquisition","Multidisciplinary Topics and Applications: MDA: Bioinformatics"],"videos":[],"references":[{"position":1,"raw":"[Allouche et al., 2014] David Allouche, Isabelle André, Sophie Barbe, Jessica Davies, Simon de Givry, George Katsirelos, Barry O'Sullivan, Steve Prestwich, Thomas Schiex, and Seydou Traoré. 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