{"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/calibrating-deep-convolutional-gaussian","title":"Calibrating Deep Convolutional Gaussian Processes","arxiv_id":"1805.10522","date":"2018-05-26","proceeding":null,"authors":["Gia-Lac Tran","Edwin V. Bonilla","John P. Cunningham","Pietro Michiardi","Maurizio Filippone"],"abstract":"The wide adoption of Convolutional Neural Networks (CNNs) in applications\nwhere decision-making under uncertainty is fundamental, has brought a great\ndeal of attention to the ability of these models to accurately quantify the\nuncertainty in their predictions. 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