Linear Recurrent

Linear recurrent neural networks (LRNNs) are a class of neural networks designed for efficient and effective processing of sequential data, aiming to overcome limitations of traditional recurrent networks in handling long sequences. Current research focuses on improving the performance and understanding of various LRNN architectures, including state-space models, linear recurrent units, and novel variations incorporating rotation matrices or gating mechanisms, often comparing them to transformers. This research is significant because LRNNs offer potential advantages in computational efficiency and scalability for tasks involving long sequences, impacting fields such as natural language processing, machine translation, and time series analysis.