Temporal Difference Type Recurrence

Temporal difference (TD) type recurrence explores how neural networks process sequential data by incorporating information from previous time steps to improve predictions or representations. Current research focuses on comparing the effectiveness of recurrence (e.g., in RNNs and state-space models) versus attention mechanisms (e.g., in Transformers) for various tasks, including time-series forecasting, video analysis, and natural language processing. This research aims to understand the strengths and limitations of different architectural choices and optimize model performance, impacting fields ranging from hydrology and medical image analysis to language modeling and animal behavior recognition. The ultimate goal is to develop more efficient and robust models capable of handling long sequences and complex temporal dependencies.