Recurrent Transformer

Recurrent Transformers aim to combine the strengths of Transformer architectures, particularly their parallel processing and attention mechanisms, with the ability of recurrent networks to model sequential data and long-term dependencies. Current research focuses on developing efficient recurrent Transformer models for various applications, including video processing, time series analysis, and long-context language modeling, often incorporating techniques like gated recurrent units, memory augmentation, and dynamic halting mechanisms to improve performance and reduce computational costs. These advancements are significant because they enable the application of Transformer-based models to tasks previously intractable due to their computational demands, leading to improvements in areas such as video denoising, smart grid management, and speech separation.