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In pursuit of faster computation, Efficient Transformers demonstrate animpressive variety of approaches -- models attaining sub-quadratic attentioncomplexity can utilize a notion of sparsity or a low-rank approximation ofinputs to reduce the number of attended keys; other ways to reduce complexityinclude locality-sensitive hashing, key pooling, additional memory to storeinformation in compacted or hybridization with other architectures, such asCNN. Often based on a strong mathematical basis, kernelized approaches allowfor the approximation of attention with linear complexity while retaining highaccuracy. Therefore, in the present paper, we aim to expand the idea oftrainable kernel methods to approximate the self-attention mechanism of theTransformer architecture.

Linear Self-Attention Approximation via Trainable Feedforward Kernel