{"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/neural-message-passing-for-multi-label-1","title":"Neural Message Passing for Multi-Label Classification","arxiv_id":"1904.08049","date":"2019-04-17","proceeding":"ICLR 2019 5","authors":["Jack Lanchantin","Arshdeep Sekhon","Yanjun Qi"],"abstract":"Multi-label classification (MLC) is the task of assigning a set of target\nlabels for a given sample. Modeling the combinatorial label interactions in MLC\nhas been a long-haul challenge. We propose Label Message Passing (LaMP) Neural\nNetworks to efficiently model the joint prediction of multiple labels. LaMP\ntreats labels as nodes on a label-interaction graph and computes the hidden\nrepresentation of each label node conditioned on the input using\nattention-based neural message passing. Attention enables LaMP to assign\ndifferent importance to neighbor nodes per label, learning how labels interact\n(implicitly). The proposed models are simple, accurate, interpretable,\nstructure-agnostic, and applicable for predicting dense labels since LaMP is\nincredibly parallelizable. We validate the benefits of LaMP on seven real-world\nMLC datasets, covering a broad spectrum of input/output types and outperforming\nthe state-of-the-art results. Notably, LaMP enables intuitive interpretation of\nhow classifying each label depends on the elements of a sample and at the same\ntime rely on its interaction with other labels. We provide our code and\ndatasets at https://github.com/QData/LaMP","url_abs":"http://arxiv.org/abs/1904.08049v1","url_pdf":"http://arxiv.org/pdf/1904.08049v1.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":"neural-message-passing-for-multi-label-1","repo_url":"https://github.com/QData/LaMP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.08049","atlas_url":"https://app.syntology.ai/?focus=1904.08049","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.08049"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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/QData/LaMP","reach":null}],"summary":{"ran_draft_wrong":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":0,"samples":[{"code_sha256_prefix":"045c1ee9f8975321","entry":"read_adj_matrix","repo":"QData/LaMP","repo_kind":"official","path":"utils/preprocess.py","file_url":"https://github.com/QData/LaMP/blob/HEAD/utils/preprocess.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"045c1ee9f8975321"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}