{"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/made-masked-autoencoder-for-distribution","title":"MADE: Masked Autoencoder for Distribution Estimation","arxiv_id":"1502.03509","date":"2015-02-12","proceeding":null,"authors":["Mathieu Germain","Karol Gregor","Iain Murray","Hugo Larochelle"],"abstract":"There has been a lot of recent interest in designing neural network models to\nestimate a distribution from a set of examples. We introduce a simple\nmodification for autoencoder neural networks that yields powerful generative\nmodels. Our method masks the autoencoder's parameters to respect autoregressive\nconstraints: each input is reconstructed only from previous inputs in a given\nordering. Constrained this way, the autoencoder outputs can be interpreted as a\nset of conditional probabilities, and their product, the full joint\nprobability. We can also train a single network that can decompose the joint\nprobability in multiple different orderings. Our simple framework can be\napplied to multiple architectures, including deep ones. Vectorized\nimplementations, such as on GPUs, are simple and fast. Experiments demonstrate\nthat this approach is competitive with state-of-the-art tractable distribution\nestimators. At test time, the method is significantly faster and scales better\nthan other autoregressive estimators.","url_abs":"http://arxiv.org/abs/1502.03509v2","url_pdf":"http://arxiv.org/pdf/1502.03509v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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