FourierNAT: a Fourier-mixing-based non-autoregressive transformer for parallel sequence generation

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Authors

Publication Details

Journal
International Journal of Complexity in Applied Science and Technology
Published
2025-01-01
DOI
https://doi.org/10.1504/ijcast.2025.10071491
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

FourierNAT: a Fourier-mixing-based non-autoregressive transformer for parallel sequence generation

Andrew Kiruluta
International Journal of Complexity in Applied Science and Technology
Neural Networks and Applications
article

FourierNAT: a Fourier-mixing-based non-autoregressive transformer for parallel sequence generation

Andrew Kiruluta
article en

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

International Journal of Complexity in Applied Science and TechnologyVol. 1(1)
Openalex Percentile: Top 95%
Neural Networks and Applications
DOIOpen Access PDFOpenAlex
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FourierNAT: a Fourier-mixing-based non-autoregressive transformer for parallel sequence generation — Andrew Kiruluta · International Journal of Complexity in Applied Science and Technology (2025) | TGRS Research Map | TGRS