Temporal Modeling

Temporal modeling focuses on representing and analyzing data that changes over time, aiming to capture dynamic patterns and dependencies within sequences. Current research emphasizes the development of robust and efficient models, including transformers, recurrent neural networks, and diffusion models, to handle diverse data types like event sequences, videos, and time series, often incorporating spatial information for enhanced performance. This field is crucial for advancing various applications, from video generation and action recognition to financial forecasting and personalized medicine, by enabling more accurate predictions and insightful analyses of dynamic systems.