{"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/machine-learning-to-do-more-with-less","title":"(Machine) Learning to Do More with Less","arxiv_id":"1706.09451","date":"2017-06-28","proceeding":null,"authors":["Timothy Cohen","Marat Freytsis","Bryan Ostdiek"],"abstract":"Determining the best method for training a machine learning algorithm is\ncritical to maximizing its ability to classify data. In this paper, we compare\nthe standard \"fully supervised\" approach (that relies on knowledge of\nevent-by-event truth-level labels) with a recent proposal that instead utilizes\nclass ratios as the only discriminating information provided during training.\nThis so-called \"weakly supervised\" technique has access to less information\nthan the fully supervised method and yet is still able to yield impressive\ndiscriminating power. In addition, weak supervision seems particularly well\nsuited to particle physics since quantum mechanics is incompatible with the\nnotion of mapping an individual event onto any single Feynman diagram. We\nexamine the technique in detail -- both analytically and numerically -- with a\nfocus on the robustness to issues of mischaracterizing the training samples.\nWeakly supervised networks turn out to be remarkably insensitive to systematic\nmismodeling. Furthermore, we demonstrate that the event level outputs for\nweakly versus fully supervised networks are probing different kinematics, even\nthough the numerical quality metrics are essentially identical. This implies\nthat it should be possible to improve the overall classification ability by\ncombining the output from the two types of networks. For concreteness, we apply\nthis technology to a signature of beyond the Standard Model physics to\ndemonstrate that all these impressive features continue to hold in a scenario\nof relevance to the LHC.","url_abs":"http://arxiv.org/abs/1706.09451v3","url_pdf":"http://arxiv.org/pdf/1706.09451v3.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":"machine-learning-to-do-more-with-less","repo_url":"https://github.com/bostdiek/PublicWeaklySupervised","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}