Global to Local Modeling

Global-to-local modeling integrates global context with localized details to improve the efficiency and accuracy of various machine learning tasks. Current research focuses on developing hierarchical architectures, such as transformers and graph-based models, that effectively combine these levels of information, often achieving significant performance gains in speed and accuracy compared to purely global or local approaches. This methodology is proving valuable across diverse applications, including natural language processing, image recognition, point cloud analysis, and solar irradiance forecasting, by enabling more robust and efficient models. The resulting improvements in computational efficiency and predictive power have significant implications for resource-constrained applications and real-time systems.