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This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. In this paper, we propose a new deep learning framework for this setting that we call Multi-Time Attention Networks. Multi-Time Attention Networks learn an embedding of continuous-time values and use an attention mechanism to produce a fixed-length representation of a time series containing a variable number of observations. We investigate the performance of this framework on interpolation and classification tasks using multiple datasets. Our results show that the proposed approach performs as well or better than a range of baseline and recently proposed models while offering significantly faster training times than current state-of-the-art methods.","url_abs":"https://arxiv.org/abs/2101.10318v2","url_pdf":"https://arxiv.org/pdf/2101.10318v2.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":"multi-time-attention-networks-for-irregularly-1","repo_url":"https://github.com/reml-lab/mTAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"multi-time-attention-networks-for-irregularly-1","repo_url":"https://github.com/WenjieDu/PyPOTS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/time-series-classification-on-physionet","task":"Time Series Classification","dataset":"PhysioNet Challenge 2012","model":"mTAND-Full","rank_in_archive_order":5,"of":28,"metrics":{"AUC":"85.8%"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-physionet","task":"Time Series Classification","dataset":"PhysioNet Challenge 2012","model":"mTAND-Enc","rank_in_archive_order":6,"of":28,"metrics":{"AUC":"85.4%"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2101.10318","atlas_url":"https://app.syntology.ai/?focus=2101.10318","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.10318"}},"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. 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