{"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/returnn-the-rwth-extensible-training","title":"RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks","arxiv_id":"1608.00895","date":"2016-08-02","proceeding":null,"authors":["Patrick Doetsch","Albert Zeyer","Paul Voigtlaender","Ilya Kulikov","Ralf Schlüter","Hermann Ney"],"abstract":"In this work we release our extensible and easily configurable neural network\ntraining software. It provides a rich set of functional layers with a\nparticular focus on efficient training of recurrent neural network topologies\non multiple GPUs. The source of the software package is public and freely\navailable for academic research purposes and can be used as a framework or as a\nstandalone tool which supports a flexible configuration. The software allows to\ntrain state-of-the-art deep bidirectional long short-term memory (LSTM) models\non both one dimensional data like speech or two dimensional data like\nhandwritten text and was used to develop successful submission systems in\nseveral evaluation campaigns.","url_abs":"http://arxiv.org/abs/1608.00895v2","url_pdf":"http://arxiv.org/pdf/1608.00895v2.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":"returnn-the-rwth-extensible-training","repo_url":"https://github.com/danenergetics/returnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"returnn-the-rwth-extensible-training","repo_url":"https://github.com/papar22/returnn-my-branch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"returnn-the-rwth-extensible-training","repo_url":"https://github.com/rwth-i6/returnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}