{"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/asymptotically-exact-inference-in","title":"Asymptotically exact inference in differentiable generative models","arxiv_id":"1605.07826","date":"2016-05-25","proceeding":null,"authors":["Matthew M. Graham","Amos J. Storkey"],"abstract":"Many generative models can be expressed as a differentiable function of\nrandom inputs drawn from some simple probability density. This framework\nincludes both deep generative architectures such as Variational Autoencoders\nand a large class of procedurally defined simulator models. We present a method\nfor performing efficient MCMC inference in such models when conditioning on\nobservations of the model output. For some models this offers an asymptotically\nexact inference method where Approximate Bayesian Computation might otherwise\nbe employed. We use the intuition that inference corresponds to integrating a\ndensity across the manifold corresponding to the set of inputs consistent with\nthe observed outputs. This motivates the use of a constrained variant of\nHamiltonian Monte Carlo which leverages the smooth geometry of the manifold to\ncoherently move between inputs exactly consistent with observations. We\nvalidate the method by performing inference tasks in a diverse set of models.","url_abs":"http://arxiv.org/abs/1605.07826v4","url_pdf":"http://arxiv.org/pdf/1605.07826v4.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":"asymptotically-exact-inference-in","repo_url":"https://github.com/matt-graham/differentiable-generative-models","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1605.07826","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.07826"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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