{"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/variational-message-passing-with-structured","title":"Variational Message Passing with Structured Inference Networks","arxiv_id":"1803.05589","date":"2018-03-15","proceeding":"ICLR 2018 1","authors":["Wu Lin","Nicolas Hubacher","Mohammad Emtiyaz Khan"],"abstract":"Recent efforts on combining deep models with probabilistic graphical models\nare promising in providing flexible models that are also easy to interpret. We\npropose a variational message-passing algorithm for variational inference in\nsuch models. We make three contributions. First, we propose structured\ninference networks that incorporate the structure of the graphical model in the\ninference network of variational auto-encoders (VAE). Second, we establish\nconditions under which such inference networks enable fast amortized inference\nsimilar to VAE. Finally, we derive a variational message passing algorithm to\nperform efficient natural-gradient inference while retaining the efficiency of\nthe amortized inference. By simultaneously enabling structured, amortized, and\nnatural-gradient inference for deep structured models, our method simplifies\nand generalizes existing methods.","url_abs":"http://arxiv.org/abs/1803.05589v2","url_pdf":"http://arxiv.org/pdf/1803.05589v2.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":"variational-message-passing-with-structured","repo_url":"https://github.com/emtiyaz/vmp-for-svae","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.05589","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.05589"}},"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/emtiyaz/vmp-for-svae","reach":null}],"summary":{"ran_violates":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"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":1,"samples":[{"code_sha256_prefix":"4f19066d9ffb9201","entry":"customized_figsize","repo":"emtiyaz/vmp-for-svae","repo_kind":"official","path":"visualisation/plots.py","file_url":"https://github.com/emtiyaz/vmp-for-svae/blob/HEAD/visualisation/plots.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4f19066d9ffb9201"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}