Kernel Transformer

Kernel transformers represent a class of neural network architectures aiming to improve the efficiency and scalability of traditional transformers, particularly for long sequences, by employing kernel methods to approximate attention mechanisms. Current research focuses on addressing limitations of existing linear transformer approaches, such as gradient instability and attention dilution, through novel normalization techniques and refined attention mechanisms, as well as exploring applications in diverse fields like long document classification and autonomous driving. These advancements offer significant potential for improving the performance and applicability of transformer models in computationally demanding tasks, impacting areas ranging from natural language processing to computer vision.