{"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/dropout-feature-ranking-for-deep-learning","title":"Dropout Feature Ranking for Deep Learning Models","arxiv_id":"1712.08645","date":"2017-12-22","proceeding":null,"authors":["Chun-Hao Chang","Ladislav Rampasek","Anna Goldenberg"],"abstract":"Deep neural networks (DNNs) achieve state-of-the-art results in a variety of\ndomains. Unfortunately, DNNs are notorious for their non-interpretability, and\nthus limit their applicability in hypothesis-driven domains such as biology and\nhealthcare. Moreover, in the resource-constraint setting, it is critical to\ndesign tests relying on fewer more informative features leading to high\naccuracy performance within reasonable budget. We aim to close this gap by\nproposing a new general feature ranking method for deep learning. We show that\nour simple yet effective method performs on par or compares favorably to eight\nstrawman, classical and deep-learning feature ranking methods in two\nsimulations and five very different datasets on tasks ranging from\nclassification to regression, in both static and time series scenarios. We also\nillustrate the use of our method on a drug response dataset and show that it\nidentifies genes relevant to the drug-response.","url_abs":"http://arxiv.org/abs/1712.08645v2","url_pdf":"http://arxiv.org/pdf/1712.08645v2.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":"dropout-feature-ranking-for-deep-learning","repo_url":"https://github.com/zzzace2000/dropout-feature-ranking","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.08645","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}