{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:36:17Z","timestamp":1723016177348},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"Spatial data are ubiquitous and have transformed decision-making in many critical domains, including public health, agriculture, transportation, etc. While recent advances in machine learning offer promising ways to harness massive spatial datasets (e.g., satellite imagery), spatial heterogeneity -- a fundamental property of spatial data -- poses a major challenge as data distributions or generative processes often vary over space. Recent studies targeting this difficult problem either require a known space-partitioning as the input, or can only support limited special cases (e.g., binary classification). Moreover, heterogeneity-pattern learned by these methods are locked to the locations of the training samples, and cannot be applied to new locations. We propose a statistically-guided framework to adaptively partition data in space during training using distribution-driven optimization and transform a deep learning model (of user's choice) into a heterogeneity-aware architecture. We also propose a spatial moderator to generalize learned patterns to new test regions. Experiment results on real-world datasets show that the framework can effectively capture footprints of heterogeneity and substantially improve prediction performances.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/752","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"5364-5368","source":"Crossref","is-referenced-by-count":0,"title":["Statistically-Guided Deep Network Transformation to Harness Heterogeneity in Space (Extended Abstract)"],"prefix":"10.24963","author":[{"given":"Yiqun","family":"Xie","sequence":"first","affiliation":[{"name":"University of Maryland"}]},{"given":"Erhu","family":"He","sequence":"additional","affiliation":[{"name":"University of Pittsburgh"}]},{"given":"Xiaowei","family":"Jia","sequence":"additional","affiliation":[{"name":"University of Pittsburgh"}]},{"given":"Han","family":"Bao","sequence":"additional","affiliation":[{"name":"University of Iowa"}]},{"given":"Xun","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Iowa"}]},{"given":"Rahul","family":"Ghosh","sequence":"additional","affiliation":[{"name":"University of Minnesota"}]},{"given":"Praveen","family":"Ravirathinam","sequence":"additional","affiliation":[{"name":"University of Minnasota"}]}],"member":"10584","event":{"number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2022","name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","start":{"date-parts":[[2022,7,23]]},"theme":"Artificial Intelligence","location":"Vienna, Austria","end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:11:25Z","timestamp":1658142685000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/752"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/752","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}