{"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/190600816","title":"Bayesian Evidential Deep Learning with PAC Regularization","arxiv_id":"1906.00816","date":"2019-06-03","proceeding":"pproximateinference AABI Symposium 2021 1","authors":["Manuel Haussmann","Sebastian Gerwinn","Melih Kandemir"],"abstract":"We propose a novel method for closed-form predictive distribution modeling with neural nets. In quantifying prediction uncertainty, we build on Evidential Deep Learning, which has been impactful as being both simple to implement and giving closed-form access to predictive uncertainty. 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