Linear Angular Attention

Linear angular attention mechanisms are emerging as efficient alternatives to traditional quadratic attention in various deep learning applications, primarily aiming to reduce computational complexity while maintaining performance. Current research focuses on integrating these methods into transformer architectures, particularly for long-context processing in language models and improving efficiency in vision transformers, often employing novel attention kernels and incorporating strategies like collinear constraints or switching between linear and quadratic attention during training and inference. This work holds significant promise for advancing large language models, improving image processing and analysis, and enabling more efficient deep learning across diverse fields by addressing the computational bottleneck of standard attention mechanisms.