Spatio Temporal Learning

Spatio-temporal learning focuses on analyzing data that changes over both space and time, aiming to understand and predict complex patterns in dynamic systems. Current research emphasizes developing efficient and robust models, including those based on large language models, graph neural networks, and recurrent neural networks, often incorporating techniques like attention mechanisms and diffusion models to capture intricate spatio-temporal dependencies. This field is crucial for numerous applications, from improving traffic prediction and urban planning to enhancing medical image analysis and environmental monitoring, driving advancements in various scientific disciplines and real-world technologies.