{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepproblog-neural-probabilistic-logic","title":"DeepProbLog: Neural Probabilistic Logic Programming","arxiv_id":"1805.10872","date":"2018-05-28","proceeding":"NeurIPS 2018 12","authors":["Robin Manhaeve","Sebastijan Dumančić","Angelika Kimmig","Thomas Demeester","Luc De Raedt"],"abstract":"We introduce DeepProbLog, a probabilistic logic programming language that\nincorporates deep learning by means of neural predicates. We show how existing\ninference and learning techniques can be adapted for the new language. Our\nexperiments demonstrate that DeepProbLog supports both symbolic and subsymbolic\nrepresentations and inference, 1) program induction, 2) probabilistic (logic)\nprogramming, and 3) (deep) learning from examples. To the best of our\nknowledge, this work is the first to propose a framework where general-purpose\nneural networks and expressive probabilistic-logical modeling and reasoning are\nintegrated in a way that exploits the full expressiveness and strengths of both\nworlds and can be trained end-to-end based on examples.","url_abs":"http://arxiv.org/abs/1805.10872v2","url_pdf":"http://arxiv.org/pdf/1805.10872v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepproblog-neural-probabilistic-logic","repo_url":"https://github.com/ml-kuleuven/deepproblog","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"deepproblog-neural-probabilistic-logic","repo_url":"https://bitbucket.org/problog/deepproblog","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deepproblog-neural-probabilistic-logic","repo_url":"https://github.com/MarcRoigVilamala/DeepProbCEP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deepproblog-neural-probabilistic-logic","repo_url":"https://github.com/dais-ita/deepprobcep","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"program-induction","task_name":"Program induction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1805.10872","atlas_url":"https://app.syntology.ai/?focus=1805.10872","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}