{"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/a-general-method-for-amortizing-variational","title":"A General Method for Amortizing Variational Filtering","arxiv_id":"1811.05090","date":"2018-11-13","proceeding":"NeurIPS 2018 12","authors":["Joseph Marino","Milan Cvitkovic","Yisong Yue"],"abstract":"We introduce the variational filtering EM algorithm, a simple,\ngeneral-purpose method for performing variational inference in dynamical latent\nvariable models using information from only past and present variables, i.e.\nfiltering. The algorithm is derived from the variational objective in the\nfiltering setting and consists of an optimization procedure at each time step.\nBy performing each inference optimization procedure with an iterative amortized\ninference model, we obtain a computationally efficient implementation of the\nalgorithm, which we call amortized variational filtering. We present\nexperiments demonstrating that this general-purpose method improves performance\nacross several deep dynamical latent variable models.","url_abs":"http://arxiv.org/abs/1811.05090v1","url_pdf":"http://arxiv.org/pdf/1811.05090v1.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":"a-general-method-for-amortizing-variational","repo_url":"https://github.com/joelouismarino/amortized-variational-filtering","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"inference-optimization","task_name":"Inference Optimization"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.05090","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.05090"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/joelouismarino/amortized-variational-filtering","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"827236cd04222fef","entry":"load_model","repo":"joelouismarino/amortized-variational-filtering","repo_kind":"official","path":"lib/models/load_model.py","file_url":"https://github.com/joelouismarino/amortized-variational-filtering/blob/HEAD/lib/models/load_model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"827236cd04222fef"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}