{"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/efficient-variational-bayesian-neural-network","title":"Efficient variational Bayesian neural network ensembles for outlier detection","arxiv_id":"1703.06749","date":"2017-03-20","proceeding":null,"authors":["Nick Pawlowski","Miguel Jaques","Ben Glocker"],"abstract":"In this work we perform outlier detection using ensembles of neural networks\nobtained by variational approximation of the posterior in a Bayesian neural\nnetwork setting. The variational parameters are obtained by sampling from the\ntrue posterior by gradient descent. We show our outlier detection results are\ncomparable to those obtained using other efficient ensembling methods.","url_abs":"http://arxiv.org/abs/1703.06749v2","url_pdf":"http://arxiv.org/pdf/1703.06749v2.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":"efficient-variational-bayesian-neural-network","repo_url":"https://github.com/pawni/sgld_online_approximation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"outlier-detection","task_name":"Outlier Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}