Methods › Natural Language Processing › Synthesized Attention Mechanisms › Factorized Dense Synthesized Attention
Factorized Dense Synthesized Attention
Introduced by Yi Tay et al. in Synthesizer: Rethinking Self-Attention in Transformer Models
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Factorized Dense Synthesized Attention is a synthesized attention mechanism, similar to dense synthesized attention, but we factorize the outputs to reduce parameters and prevent overfitting. It was proposed as part of the Synthesizer architecture. The factorized variant of the dense synthesizer can be expressed as follows:
A, B = F_A(Xᵢ), F_B(Xᵢ)
where F_A(.) projects input Xᵢ into a dimensions, F_B(.) projects Xᵢ to b dimensions, and a x b = l. The output of the factorized module is now written as:
Y = Softmax(C)G(X)
where C = H_A(A) * H_B(B), where H_A, H_B are tiling functions and C ∈ℝ^(l x l). The tiling function simply duplicates the vector k times, i.e., ℝˡ →ℝˡᵏ. In this case, H_A() is a projection of ℝᵃ →ℝᵃᵇ and H_B() is a projection of ℝᵇ →ℝᵇᵃ. To avoid having similar values within the same block, we compose the outputs of H_A and H_B.
Papers archive 2025-07-28
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Synthesizer: Rethinking Self-Attention in Transformer Models 2 May 2020 · 1 repository · arXiv:2005.00743Syntology ran 1 of 1 samples · 0 unverified
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