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Computer Science > Machine Learning

arXiv:2502.18394 (cs)
[Submitted on 25 Feb 2025]

Title:The FFT Strikes Back: An Efficient Alternative to
Self-Attention

Authors:Jacob Fein-Ashley
View a PDF of the paper titled The FFT Strikes Back: An Efficient
Alternative to Self-Attention, by Jacob Fein-Ashley
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    Abstract:Conventional self-attention mechanisms incur quadratic
    complexity, limiting their scalability on long sequences. We
    introduce FFTNet, an adaptive spectral filtering framework that
    leverages the Fast Fourier Transform (FFT) to achieve global
    token mixing in $\mathcal{O}(n\log n)$ time. By transforming
    inputs into the frequency domain, FFTNet exploits the
    orthogonality and energy preservation guaranteed by Parseval's
    theorem to capture long-range dependencies efficiently. A
    learnable spectral filter and modReLU activation dynamically
    emphasize salient frequency components, providing a rigorous and
    adaptive alternative to traditional self-attention. Experiments
    on the Long Range Arena and ImageNet benchmarks validate our
    theoretical insights and demonstrate superior performance over
    fixed Fourier and standard attention models.

Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2502.18394 [cs.LG]
          (or arXiv:2502.18394v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2502.18394
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jacob Fein-Ashley [view email]
[v1] Tue, 25 Feb 2025 17:43:43 UTC (366 KB)
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