{"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/deeppink-reproducible-feature-selection-in","title":"DeepPINK: reproducible feature selection in deep neural networks","arxiv_id":"1809.01185","date":"2018-09-04","proceeding":"NeurIPS 2018 12","authors":["Yang Young Lu","Yingying Fan","Jinchi Lv","William Stafford Noble"],"abstract":"Deep learning has become increasingly popular in both supervised and\nunsupervised machine learning thanks to its outstanding empirical performance.\nHowever, because of their intrinsic complexity, most deep learning methods are\nlargely treated as black box tools with little interpretability. Even though\nrecent attempts have been made to facilitate the interpretability of deep\nneural networks (DNNs), existing methods are susceptible to noise and lack of\nrobustness.\n  Therefore, scientists are justifiably cautious about the reproducibility of\nthe discoveries, which is often related to the interpretability of the\nunderlying statistical models. In this paper, we describe a method to increase\nthe interpretability and reproducibility of DNNs by incorporating the idea of\nfeature selection with controlled error rate. By designing a new DNN\narchitecture and integrating it with the recently proposed knockoffs framework,\nwe perform feature selection with a controlled error rate, while maintaining\nhigh power. This new method, DeepPINK (Deep feature selection using\nPaired-Input Nonlinear Knockoffs), is applied to both simulated and real data\nsets to demonstrate its empirical utility.","url_abs":"http://arxiv.org/abs/1809.01185v2","url_pdf":"http://arxiv.org/pdf/1809.01185v2.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":"deeppink-reproducible-feature-selection-in","repo_url":"https://github.com/younglululu/DeepPINK","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"GPL-2.0"}}],"tasks":[{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.01185","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}