{"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/rnn-recurrent-library-for-torch","title":"rnn : Recurrent Library for Torch","arxiv_id":"1511.07889","date":"2015-11-24","proceeding":null,"authors":["Nicholas Léonard","Sagar Waghmare","Yang Wang","Jin-Hwa Kim"],"abstract":"The rnn package provides components for implementing a wide range of\nRecurrent Neural Networks. It is built withing the framework of the Torch\ndistribution for use with the nn package. The components have evolved from 3\niterations, each adding to the flexibility and capability of the package. All\ncomponent modules inherit either the AbstractRecurrent or AbstractSequencer\nclasses. Strong unit testing, continued backwards compatibility and access to\nsupporting material are the principles followed during its development. The\npackage is compared against existing implementations of two published papers.","url_abs":"http://arxiv.org/abs/1511.07889v2","url_pdf":"http://arxiv.org/pdf/1511.07889v2.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":"rnn-recurrent-library-for-torch","repo_url":"https://github.com/Element-Research/rnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"torch","reach":null}],"tasks":[],"methods":[{"method_slug":"1-bit-adam","method_name":"1-bit Adam"},{"method_slug":"adam","method_name":"Adam"}],"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}