Local Dependency

Local dependency, the influence of nearby elements on a system's behavior, is a crucial research area across diverse fields, aiming to improve model accuracy and efficiency by effectively capturing these interactions. Current research focuses on developing algorithms and architectures, such as graph neural networks and transformer-based models, that can efficiently model both local and global dependencies, particularly in complex systems with large state spaces or long-range interactions. This work has significant implications for various applications, including improved reinforcement learning, more accurate brain age prediction, and enhanced performance in natural language processing and computer vision tasks.