{"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/fast-counting-in-machine-learning","title":"Fast Counting in Machine Learning Applications","arxiv_id":"1804.04640","date":"2018-04-12","proceeding":null,"authors":["Subhadeep Karan","Matthew Eichhorn","Blake Hurlburt","Grant Iraci","Jaroslaw Zola"],"abstract":"We propose scalable methods to execute counting queries in machine learning\napplications. To achieve memory and computational efficiency, we abstract\ncounting queries and their context such that the counts can be aggregated as a\nstream. We demonstrate performance and scalability of the resulting approach on\nrandom queries, and through extensive experimentation using Bayesian networks\nlearning and association rule mining. Our methods significantly outperform\ncommonly used ADtrees and hash tables, and are practical alternatives for\nprocessing large-scale data.","url_abs":"http://arxiv.org/abs/1804.04640v3","url_pdf":"http://arxiv.org/pdf/1804.04640v3.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":"fast-counting-in-machine-learning","repo_url":"https://gitlab.com/SCoRe-Group/SABNAtk","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"fast-counting-in-machine-learning","repo_url":"https://gitlab.com/SCoRe-Group/SABNAtk-Benchmarks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"fast-counting-in-machine-learning","repo_url":"https://github.com/omerjerk/cuSABNAtk","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"fast-counting-in-machine-learning","repo_url":"https://gitlab.com/SCoRe-Group/SABNA-Release","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}