Linear RNN

Linear recurrent neural networks (RNNs) are a class of neural networks designed for efficient sequential data processing, aiming to overcome the computational limitations of traditional RNNs and transformers while maintaining strong performance. Current research focuses on developing and refining architectures like Mamba, HGRN, and RWKV, often incorporating techniques such as gating mechanisms, state expansion, and test-time training to improve accuracy and scalability across diverse applications. These advancements are significant because linear RNNs offer a compelling alternative for handling long sequences in tasks like time series forecasting, language modeling, and 3D object detection, providing a balance between computational efficiency and predictive power.