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FNetAR: Mixing Tokens with Autoregressive Fourier Transforms
[article]
2021
arXiv
pre-print
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
arXiv:2107.10932v1
fatcat:m4hgxfs7qrg7hfbdcigaxjq4k4