Traditional RNNs

Traditional recurrent neural networks (RNNs) process sequential data by maintaining an internal state that is updated at each time step, aiming to capture temporal dependencies. Current research focuses on improving RNN learnability, particularly for long sequences, exploring architectures like LSTMs and GRUs, and investigating novel designs such as Mamba and RWKV that address limitations in computational efficiency and long-term memory. These efforts are driven by the need for more robust and efficient sequence models with improved interpretability, impacting diverse fields including time series forecasting, natural language processing, and image analysis.