{"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/learning-stochastic-recurrent-networks","title":"Learning Stochastic Recurrent Networks","arxiv_id":"1411.7610","date":"2014-11-27","proceeding":null,"authors":["Justin Bayer","Christian Osendorfer"],"abstract":"Leveraging advances in variational inference, we propose to enhance recurrent\nneural networks with latent variables, resulting in Stochastic Recurrent\nNetworks (STORNs). The model i) can be trained with stochastic gradient\nmethods, ii) allows structured and multi-modal conditionals at each time step,\niii) features a reliable estimator of the marginal likelihood and iv) is a\ngeneralisation of deterministic recurrent neural networks. We evaluate the\nmethod on four polyphonic musical data sets and motion capture data.","url_abs":"http://arxiv.org/abs/1411.7610v3","url_pdf":"http://arxiv.org/pdf/1411.7610v3.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":"learning-stochastic-recurrent-networks","repo_url":"https://github.com/dgedon/DeepSSM_SysID","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1411.7610","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}