FNetAR: Mixing Tokens with Autoregressive Fourier Transforms [article]

Tim Lou, Michael Park, Mohammad Ramezanali, Vincent Tang
<span title="2021-07-22">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this note we examine the autoregressive generalization of the FNet algorithm, in which self-attention layers from the standard Transformer architecture are substituted with a trivial sparse-uniformsampling procedure based on Fourier transforms. Using the Wikitext-103 benchmark, we demonstratethat FNetAR retains state-of-the-art performance (25.8 ppl) on the task of causal language modelingcompared to a Transformer-XL baseline (24.2 ppl) with only half the number self-attention layers,thus
more &raquo; ... viding further evidence for the superfluity of deep neural networks with heavily compoundedattention mechanisms. The autoregressive Fourier transform could likely be used for parameterreduction on most Transformer-based time-series prediction models.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.10932v1">arXiv:2107.10932v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m4hgxfs7qrg7hfbdcigaxjq4k4">fatcat:m4hgxfs7qrg7hfbdcigaxjq4k4</a> </span>
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