{"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/lanistr-multimodal-learning-from-structured","title":"LANISTR: Multimodal Learning from Structured and Unstructured Data","arxiv_id":"2305.16556","date":"2023-05-26","proceeding":null,"authors":["Sayna Ebrahimi","Sercan O. Arik","Yihe Dong","Tomas Pfister"],"abstract":"Multimodal large-scale pretraining has shown impressive performance for unstructured data such as language and image. However, a prevalent real-world scenario involves structured data types, tabular and time-series, along with unstructured data. Such scenarios have been understudied. To bridge this gap, we propose LANISTR, an attention-based framework to learn from LANguage, Image, and STRuctured data. The core of LANISTR's methodology is rooted in \\textit{masking-based} training applied across both unimodal and multimodal levels. In particular, we introduce a new similarity-based multimodal masking loss that enables it to learn cross-modal relations from large-scale multimodal data with missing modalities. On two real-world datasets, MIMIC-IV (from healthcare) and Amazon Product Review (from retail), LANISTR demonstrates remarkable improvements, 6.6\\% (in AUROC) and 14\\% (in accuracy) when fine-tuned with 0.1\\% and 0.01\\% of labeled data, respectively, compared to the state-of-the-art alternatives. Notably, these improvements are observed even with very high ratio of samples (35.7\\% and 99.8\\% respectively) not containing all modalities, underlining the robustness of LANISTR to practical missing modality challenge. Our code and models will be available at https://github.com/google-research/lanistr","url_abs":"https://arxiv.org/abs/2305.16556v3","url_pdf":"https://arxiv.org/pdf/2305.16556v3.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":"lanistr-multimodal-learning-from-structured","repo_url":"https://github.com/google-research/lanistr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2305.16556","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}