{"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/relnn-a-deep-neural-model-for-relational","title":"RelNN: A Deep Neural Model for Relational Learning","arxiv_id":"1712.02831","date":"2017-12-07","proceeding":null,"authors":["Seyed Mehran Kazemi","David Poole"],"abstract":"Statistical relational AI (StarAI) aims at reasoning and learning in noisy\ndomains described in terms of objects and relationships by combining\nprobability with first-order logic. With huge advances in deep learning in the\ncurrent years, combining deep networks with first-order logic has been the\nfocus of several recent studies. Many of the existing attempts, however, only\nfocus on relations and ignore object properties. The attempts that do consider\nobject properties are limited in terms of modelling power or scalability. In\nthis paper, we develop relational neural networks (RelNNs) by adding hidden\nlayers to relational logistic regression (the relational counterpart of\nlogistic regression). We learn latent properties for objects both directly and\nthrough general rules. Back-propagation is used for training these models. A\nmodular, layer-wise architecture facilitates utilizing the techniques developed\nwithin deep learning community to our architecture. Initial experiments on\neight tasks over three real-world datasets show that RelNNs are promising\nmodels for relational learning.","url_abs":"http://arxiv.org/abs/1712.02831v1","url_pdf":"http://arxiv.org/pdf/1712.02831v1.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":"relnn-a-deep-neural-model-for-relational","repo_url":"https://github.com/Mehran-k/RelNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"relational-reasoning","task_name":"Relational Reasoning"},{"task_slug":"model","task_name":"model"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1712.02831","atlas_url":"https://app.syntology.ai/?focus=1712.02831","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}