{"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/safe-semi-supervised-learning-of-sum-product","title":"Safe Semi-Supervised Learning of Sum-Product Networks","arxiv_id":"1710.03444","date":"2017-10-10","proceeding":null,"authors":["Martin Trapp","Tamas Madl","Robert Peharz","Franz Pernkopf","Robert Trappl"],"abstract":"In several domains obtaining class annotations is expensive while at the same\ntime unlabelled data are abundant. While most semi-supervised approaches\nenforce restrictive assumptions on the data distribution, recent work has\nmanaged to learn semi-supervised models in a non-restrictive regime. However,\nso far such approaches have only been proposed for linear models. In this work,\nwe introduce semi-supervised parameter learning for Sum-Product Networks\n(SPNs). SPNs are deep probabilistic models admitting inference in linear time\nin number of network edges. Our approach has several advantages, as it (1)\nallows generative and discriminative semi-supervised learning, (2) guarantees\nthat adding unlabelled data can increase, but not degrade, the performance\n(safe), and (3) is computationally efficient and does not enforce restrictive\nassumptions on the data distribution. We show on a variety of data sets that\nsafe semi-supervised learning with SPNs is competitive compared to\nstate-of-the-art and can lead to a better generative and discriminative\nobjective value than a purely supervised approach.","url_abs":"http://arxiv.org/abs/1710.03444v1","url_pdf":"http://arxiv.org/pdf/1710.03444v1.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":"safe-semi-supervised-learning-of-sum-product","repo_url":"https://github.com/trappmartin/SSLSPN_UAI2017","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}