{"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/inference-networks-for-sequential-monte-carlo","title":"Inference Networks for Sequential Monte Carlo in Graphical Models","arxiv_id":"1602.06701","date":"2016-02-22","proceeding":null,"authors":["Brooks Paige","Frank Wood"],"abstract":"We introduce a new approach for amortizing inference in directed graphical\nmodels by learning heuristic approximations to stochastic inverses, designed\nspecifically for use as proposal distributions in sequential Monte Carlo\nmethods. We describe a procedure for constructing and learning a structured\nneural network which represents an inverse factorization of the graphical\nmodel, resulting in a conditional density estimator that takes as input\nparticular values of the observed random variables, and returns an\napproximation to the distribution of the latent variables. This recognition\nmodel can be learned offline, independent from any particular dataset, prior to\nperforming inference. The output of these networks can be used as\nautomatically-learned high-quality proposal distributions to accelerate\nsequential Monte Carlo across a diverse range of problem settings.","url_abs":"http://arxiv.org/abs/1602.06701v2","url_pdf":"http://arxiv.org/pdf/1602.06701v2.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":"inference-networks-for-sequential-monte-carlo","repo_url":"https://github.com/tbrx/compiled-inference","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1602.06701","atlas_url":"https://app.syntology.ai/?focus=1602.06701","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}