Tensor Attention

Tensor attention mechanisms aim to improve upon traditional attention by capturing higher-order relationships within data, going beyond the pairwise comparisons of standard matrix-based attention. Current research focuses on developing efficient algorithms to overcome the computational complexity of these higher-order interactions, exploring architectures like graph-based transformers and employing techniques such as tensor decomposition and rank-1 approximations to reduce computational cost. These advancements are significant because they enable the application of tensor attention to larger datasets and more complex tasks, improving performance in areas such as image classification and spiking neural networks.