Low Rank Attention

Low-rank attention aims to improve the efficiency and scalability of attention mechanisms, crucial components of many machine learning models, by reducing the computational complexity associated with large attention matrices. Current research focuses on developing novel architectures like linear attention and state-space models (e.g., Mamba), employing low-rank approximations within existing attention modules (e.g., through low-rank decomposition or side-tuning), and analyzing the interplay between attention rank, head count, and model depth. These advancements are significant because they enable the application of attention-based models to longer sequences and higher-dimensional data, impacting various fields including computer vision, natural language processing, and speech recognition through faster training and inference.