{"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/pac-bayesian-margin-bounds-for-convolutional","title":"PAC-Bayesian Margin Bounds for Convolutional Neural Networks","arxiv_id":"1801.00171","date":"2017-12-30","proceeding":null,"authors":["Konstantinos Pitas","Mike Davies","Pierre Vandergheynst"],"abstract":"Recently the generalization error of deep neural networks has been analyzed\nthrough the PAC-Bayesian framework, for the case of fully connected layers. We\nadapt this approach to the convolutional setting.","url_abs":"http://arxiv.org/abs/1801.00171v2","url_pdf":"http://arxiv.org/pdf/1801.00171v2.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":"pac-bayesian-margin-bounds-for-convolutional","repo_url":"https://github.com/konstantinos-p/PAC_Bayesian_Generalization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.00171","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}