{"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/variational-recurrent-auto-encoders","title":"Variational Recurrent Auto-Encoders","arxiv_id":"1412.6581","date":"2014-12-20","proceeding":null,"authors":["Otto Fabius","Joost R. van Amersfoort"],"abstract":"In this paper we propose a model that combines the strengths of RNNs and\nSGVB: the Variational Recurrent Auto-Encoder (VRAE). Such a model can be used\nfor efficient, large scale unsupervised learning on time series data, mapping\nthe time series data to a latent vector representation. The model is\ngenerative, such that data can be generated from samples of the latent space.\nAn important contribution of this work is that the model can make use of\nunlabeled data in order to facilitate supervised training of RNNs by\ninitialising the weights and network state.","url_abs":"http://arxiv.org/abs/1412.6581v6","url_pdf":"http://arxiv.org/pdf/1412.6581v6.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":"variational-recurrent-auto-encoders","repo_url":"https://github.com/abhmalik/timeseries-clustering-vae","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"variational-recurrent-auto-encoders","repo_url":"https://github.com/arunesh-mittal/VariationalRecurrentAutoEncoder","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"variational-recurrent-auto-encoders","repo_url":"https://github.com/sobhan-moosavi/DCRNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"variational-recurrent-auto-encoders","repo_url":"https://github.com/tejaslodaya/timeseries-clustering-vae","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"variational-recurrent-auto-encoders","repo_url":"https://github.com/y0ast/variational-recurrent-autoencoder","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1412.6581","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}