Factorized Attention

Factorized attention aims to improve the efficiency of the self-attention mechanism in Transformer networks, addressing its quadratic computational complexity which limits scalability to large inputs. Current research focuses on developing novel architectures and algorithms, such as factorized kernels and parameterized mixing links, to reduce this complexity to linear or near-linear time, often employing sparse matrices and optimized memory access. These advancements are significant because they enable the application of Transformer-based models to larger datasets and more complex tasks across diverse fields, including weather forecasting, social network analysis, and computer vision, where computational efficiency is crucial.