{"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/bayesian-sparsification-of-recurrent-neural","title":"Bayesian Sparsification of Recurrent Neural Networks","arxiv_id":"1708.00077","date":"2017-07-31","proceeding":null,"authors":["Ekaterina Lobacheva","Nadezhda Chirkova","Dmitry Vetrov"],"abstract":"Recurrent neural networks show state-of-the-art results in many text analysis\ntasks but often require a lot of memory to store their weights. Recently\nproposed Sparse Variational Dropout eliminates the majority of the weights in a\nfeed-forward neural network without significant loss of quality. We apply this\ntechnique to sparsify recurrent neural networks. To account for recurrent\nspecifics we also rely on Binary Variational Dropout for RNN. We report 99.5%\nsparsity level on sentiment analysis task without a quality drop and up to 87%\nsparsity level on language modeling task with slight loss of accuracy.","url_abs":"http://arxiv.org/abs/1708.00077v1","url_pdf":"http://arxiv.org/pdf/1708.00077v1.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":"bayesian-sparsification-of-recurrent-neural","repo_url":"https://github.com/tipt0p/SparseBayesianRNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"bayesian-sparsification-of-recurrent-neural","repo_url":"https://github.com/ars-ashuha/variational-dropout-sparsifies-dnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"variational-dropout","method_name":"Variational Dropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.00077","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}