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Computer Science > Machine Learning

arXiv:2403.01643 (cs)
[Submitted on 3 Mar 2024]

Title:You Need to Pay Better Attention

Authors:Mehran Hosseini, Peyman Hosseini
View a PDF of the paper titled You Need to Pay Better Attention, by
Mehran Hosseini and 1 other authors
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    Abstract:We introduce three new attention mechanisms that
    outperform standard multi-head attention in terms of efficiency
    and learning capabilities, thereby improving the performance and
    broader deployability of Transformer models. Our first
    contribution is Optimised Attention, which performs similarly to
    standard attention, but has 3/4 as many parameters and one matrix
    multiplication fewer per head. Next, we introduce Efficient
    Attention, which performs on par with standard attention with
    only 1/2 as many parameters as many parameters and two matrix
    multiplications fewer per head and is up to twice as fast as
    standard attention. Lastly, we introduce Super Attention, which
    surpasses standard attention by a significant margin in both
    vision and natural language processing tasks while having fewer
    parameters and matrix multiplications. In addition to providing
    rigorous mathematical comparisons, we evaluate the presented
    attention mechanisms on MNIST, CIFAR100, IMDB Movie Reviews, and
    Amazon Reviews datasets.

             Machine Learning (cs.LG); Artificial Intelligence
Subjects:    (cs.AI); Computation and Language (cs.CL); Computer
             Vision and Pattern Recognition (cs.CV)
MSC classes: 68T07 (Primary) 68T45, 68T50, 68T10, 15A03, 15A04
             (Secondary)
ACM classes: I.2.6; I.2.7; I.2.10; I.4.0; I.5.0; I.7.0
Cite as:     arXiv:2403.01643 [cs.LG]
             (or arXiv:2403.01643v1 [cs.LG] for this version)
             https://doi.org/10.48550/arXiv.2403.01643
             Focus to learn more
             arXiv-issued DOI via DataCite

Submission history

From: Mehran Hosseini [view email]
[v1] Sun, 3 Mar 2024 23:40:35 UTC (78 KB)
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