首页|Data from University of Michigan Provide New Insights into Machine Learning (PM2.5 forecasting under distribution shift: A graph learning approach)
Data from University of Michigan Provide New Insights into Machine Learning (PM2.5 forecasting under distribution shift: A graph learning approach)
扫码查看
点击上方二维码区域,可以放大扫码查看
原文链接
NETL
NSTL
Investigators publish new report on artificial intelligence. According to news originating from the University of Michigan by NewsRx correspondents, research stated, “We present a new benchmark task for graph-based machine learning, aiming to predict future air quality (PM2.5 concentration) observed by a geographically distributed network of environmental sensors.” The news correspondents obtained a quote from the research from University of Michigan: “While prior work has successfully applied Graph Neural Networks (GNNs) on a wide family of spatio-temporal prediction tasks, the new benchmark task introduced here brings a technical challenge that has been less studied in the context of graph-based spatio-temporal learning: distribution shift across a long period of time. An important goal of this paper is to understand the behavior of spatio-temporal GNNs under distribution shift. We conduct a comprehensive comparative study of both graph-based and non-graph-based machine learning models under two data split methods, one results in distribution shift and one does not.”
University of MichiganCyborgsEmerging TechnologiesGraph LearningMachine Learning