Random Feature Attention

Random Feature Attention (RFA) aims to accelerate attention mechanisms, a core component of Transformer networks, by approximating the computationally expensive softmax function using random features. Current research focuses on developing more accurate and efficient RFA methods, such as those employing Maclaurin features or query-specific distributions, and analyzing their theoretical properties, including generalization capabilities and sample complexity. These advancements offer the potential to significantly improve the scalability and efficiency of Transformer models for various applications, particularly those involving long sequences or large datasets.