{"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/importance-weighted-autoencoders","title":"Importance Weighted Autoencoders","arxiv_id":"1509.00519","date":"2015-09-01","proceeding":null,"authors":["Yuri Burda","Roger Grosse","Ruslan Salakhutdinov"],"abstract":"The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently\nproposed generative model pairing a top-down generative network with a\nbottom-up recognition network which approximates posterior inference. 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