Learned Queries and Keys Are All You Need: Replacing the Value Projection with Structured Transforms

Abstract:To reduce the number of parameters and cache memory requirements of transformers we introduce dual-headed transformers instead of three heads. We studied Walsh-Hadamard Transform (WHT), Discrete Cosine Transform (DCT), Discrete Fourier Transform, filterbank based Shearlet Transform, and Multiplication-Avoiding (MA) operators to construct dual heads. We combine spatial patches and their orthogonal transforms (or Shearlet and MA operators) in a structure similar to the attention block. We obtained better results than triple headed transformers in ImageNet. Extensive simulation examples are presented.

Machine Learning (cs.LG)

arXiv:2609.36698 [cs.LG]

(or arXiv:2609.36698v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.36698 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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