{"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/a-deep-learning-approach-to-unsupervised","title":"A Deep Learning Approach to Unsupervised Ensemble Learning","arxiv_id":"1602.02285","date":"2016-02-06","proceeding":null,"authors":["Uri Shaham","Xiuyuan Cheng","Omer Dror","Ariel Jaffe","Boaz Nadler","Joseph Chang","Yuval Kluger"],"abstract":"We show how deep learning methods can be applied in the context of\ncrowdsourcing and unsupervised ensemble learning. First, we prove that the\npopular model of Dawid and Skene, which assumes that all classifiers are\nconditionally independent, is {\\em equivalent} to a Restricted Boltzmann\nMachine (RBM) with a single hidden node. Hence, under this model, the posterior\nprobabilities of the true labels can be instead estimated via a trained RBM.\nNext, to address the more general case, where classifiers may strongly violate\nthe conditional independence assumption, we propose to apply RBM-based Deep\nNeural Net (DNN). Experimental results on various simulated and real-world\ndatasets demonstrate that our proposed DNN approach outperforms other\nstate-of-the-art methods, in particular when the data violates the conditional\nindependence assumption.","url_abs":"http://arxiv.org/abs/1602.02285v1","url_pdf":"http://arxiv.org/pdf/1602.02285v1.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":"a-deep-learning-approach-to-unsupervised","repo_url":"https://github.com/ushaham/RBMpaper","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1602.02285","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}