{"type":"ScholarlyArticle","title":"NeuPSL: Neural Probabilistic Soft Logic","authors":[{"name":"Connor Pryor","slug":"connor-pryor","given":"Connor","family":"Pryor","affiliation":"UC Santa Barbara"},{"name":"Charles Dickens","slug":"charles-dickens","given":"Charles","family":"Dickens","affiliation":"UC Santa Barbara"},{"name":"Eriq Augustine","slug":"eriq-augustine","given":"Eriq","family":"Augustine","affiliation":"UC Santa Barbara"},{"name":"Alon Albalak","slug":"alon-albalak","given":"Alon","family":"Albalak","affiliation":"UC Santa Barbara"},{"name":"William Yang Wang","slug":"william-yang-wang","given":"William Yang","family":"Wang","affiliation":"UC Santa Barbara"},{"name":"Lise Getoor","slug":"lise-getoor","given":"Lise","family":"Getoor","affiliation":"UC Santa Barbara"}],"abstract":"In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks. To model the boundary between neural and symbolic representations, we propose a family of energy-based models, NeSy Energy-Based Models, and show that they are general enough to include NeuPSL and many other NeSy approaches. Using this framework, we show how to seamlessly integrate neural and symbolic parameter learning and inference in NeuPSL. Through an extensive empirical evaluation, we demonstrate the benefits of using NeSy methods, achieving upwards of 30% improvement over independent neural network models. On a well-established NeSy task, MNIST-Addition, NeuPSL demonstrates its joint reasoning capabilities by outperforming existing NeSy approaches by up to 10% in low-data settings. Furthermore, NeuPSL achieves a 5% boost in performance over state-of-the-art NeSy methods in a canonical citation network task with up to a 40 times speed up.","doi":"10.24963/ijcai.2023/461","url":"https://ijcai.org/proceedings/2023/461","pdf":"https://ijcai.org/proceedings/2023/0461.pdf","markdown":"https://ijcai.org/proceedings/2023/461.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/461","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":461,"pages":{"first":4145,"last":4153},"keywords":["Machine Learning: ML: Structured prediction","Machine Learning: ML: Symbolic methods"],"videos":[],"references":[{"position":1,"raw":"[Alshukaili et al., 2016] Duhai Alshukaili, Alvarao Fernan- dees, and Norman Paton. 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