{"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/the-hybrid-bootstrap-a-drop-in-replacement","title":"The Hybrid Bootstrap: A Drop-in Replacement for Dropout","arxiv_id":"1801.07316","date":"2018-01-22","proceeding":null,"authors":["Robert Kosar","David W. Scott"],"abstract":"Regularization is an important component of predictive model building. The\nhybrid bootstrap is a regularization technique that functions similarly to\ndropout except that features are resampled from other training points rather\nthan replaced with zeros. We show that the hybrid bootstrap offers superior\nperformance to dropout. We also present a sampling based technique to simplify\nhyperparameter choice. Next, we provide an alternative sampling technique for\nconvolutional neural networks. Finally, we demonstrate the efficacy of the\nhybrid bootstrap on non-image tasks using tree-based models.","url_abs":"http://arxiv.org/abs/1801.07316v1","url_pdf":"http://arxiv.org/pdf/1801.07316v1.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":"the-hybrid-bootstrap-a-drop-in-replacement","repo_url":"https://github.com/r-kosar/hybrid_bootstrap","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"the-hybrid-bootstrap-a-drop-in-replacement","repo_url":"https://github.com/bluesky314/Porto-Seguro-s-Safe-Driver-Prediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"the-hybrid-bootstrap-a-drop-in-replacement","repo_url":"https://github.com/bluesky314/Representation-Learning-Porto-Seguro-s-Safe-Driver-Prediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}