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Sequence Prediction Using Spectral RNNs

 
: Wolter, Moritz; Gall, Jürgen; Yao, Angela

:

Farkaš, I. ; European Neural Network Society:
Artificial Neural Networks and Machine Learning - ICANN 2020. Proceedings. Pt.I : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020
Cham: Springer Nature, 2020 (Lecture Notes in Computer Science 12396)
ISBN: 978-3-030-61608-3 (Print)
ISBN: 978-3-030-61609-0 (Online)
S.825-837
International Conference on Artificial Neural Networks (ICANN) <29, 2020, Online>
Englisch
Konferenzbeitrag
Fraunhofer SCAI ()
sequence modelling; frequency domain; short time fourier transform

Abstract
Fourier methods have a long and proven track record as an excellent tool in data processing. As memory and computational constraints gain importance in embedded and mobile applications, we propose to combine Fourier methods and recurrent neural network architectures. The short-time Fourier transform allows us to efficiently process multiple samples at a time. Additionally, weight reductions trough low pass filtering is possible. We predict time series data drawn from the chaotic Mackey-Glass differential equation and real-world power load and motion capture data.

: http://publica.fraunhofer.de/dokumente/N-618899.html