{"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/gate-variants-of-gated-recurrent-unit-gru","title":"Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks","arxiv_id":"1701.05923","date":"2017-01-20","proceeding":null,"authors":["Rahul Dey","Fathi M. Salem"],"abstract":"The paper evaluates three variants of the Gated Recurrent Unit (GRU) in\nrecurrent neural networks (RNN) by reducing parameters in the update and reset\ngates. We evaluate the three variant GRU models on MNIST and IMDB datasets and\nshow that these GRU-RNN variant models perform as well as the original GRU RNN\nmodel while reducing the computational expense.","url_abs":"http://arxiv.org/abs/1701.05923v1","url_pdf":"http://arxiv.org/pdf/1701.05923v1.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":"gate-variants-of-gated-recurrent-unit-gru","repo_url":"https://github.com/abhaskumarsinha/GRU-varients","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"model-optimization","task_name":"Model Optimization"}],"methods":[{"method_slug":"gru","method_name":"GRU"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1701.05923","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}