{"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-simple-neural-model-for-unlabelled-data","title":"A simple neural model for unlabelled data","arxiv_id":null,"date":"2020-03-13","proceeding":"Preprint 2020 3","authors":["Ünver Çiftçi"],"abstract":"Neural networks are powerful tools for modelling big unstructured data. Advanced architectures and learning methods are used in applications. Nevertheless simpler models which use less data could be helpful. We propose a very simple learning algorithm in order to model the data distribution. Experiments show how effective and data-efficient our method is. Code is available at https://github.com/unverciftci/toUniform.","url_abs":"https://www.researchsquare.com/article/rs-2671009/v1","url_pdf":"https://assets.researchsquare.com/files/rs-2671009/v1_covered.pdf?c=1678860658","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-simple-neural-model-for-unlabelled-data","repo_url":"https://github.com/unverciftci/toUniform","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}