{"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/a-multi-instance-deep-neural-network","title":"A multi-instance deep neural network classifier: application to Higgs boson CP measurement","arxiv_id":"1803.00838","date":"2018-03-02","proceeding":null,"authors":["P. Bialas","D. Nemeth","E. Richter-Wąs"],"abstract":"We investigate properties of a classifier applied to the measurements of the\nCP state of the Higgs boson in $H\\rightarrow\\tau\\tau$ decays. The problem is\nframed as binary classifier applied to individual instances. Then the prior\nknowledge that the instances belong to the same class is used to define the\nmulti-instance classifier. Its final score is calculated as multiplication of\nsingle instance scores for a given series of instances. In the paper we discuss\nproperties of such classifier, notably its dependence on the number of\ninstances in the series. This classifier exhibits very strong random dependence\non the number of epochs used for training and requires careful tuning of the\nclassification threshold. We derive formula for this optimal threshold.","url_abs":"http://arxiv.org/abs/1803.00838v2","url_pdf":"http://arxiv.org/pdf/1803.00838v2.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":"a-multi-instance-deep-neural-network","repo_url":"https://github.com/klasocha/HiggsCP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}