Linear Attention

Linear attention mechanisms aim to improve the efficiency of Transformer models by reducing the computational complexity of the attention operation from quadratic to linear time and space with respect to sequence length. Current research focuses on developing novel linear attention architectures, such as Mamba and Gated Linear Attention, and integrating them into various applications, including language modeling, image generation, and time series forecasting, often through techniques like kernelization or state space modeling. These advancements offer significant potential for scaling up Transformer-based models to handle longer sequences and higher-resolution data, thereby impacting diverse fields requiring efficient processing of large datasets.