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- W2873418536 abstract "In this paper, we derived the online additive updates of gated recurrent unit (GRU) network by using fast fourier transform-inverse fast fourier transform (FFT-IFFT) operator. In the gating process of the GRU networks, we work in the frequency domain and execute all the linear operations. For the non-linear functions in the gating process, we first shift back to the time domain and then apply non-linear GRU gating functions. Furthermore, in order to reduce the computational complexity and speed up the training process, we apply weight matrix factorization (WMF) on the FFT-IFFT variant GRU network. We then compute the online additive updates of the FFT-WMF based GRU networks using stochastic gradient descent (SGD) algorithm. We also used long short-term memory (LSTM) networks in place of the GRU networks. Through an extensive set of experiments, we illustrate that our proposed algorithm achieves a significant increase in performance with a decrease in computational complexity." @default.
- W2873418536 created "2018-07-19" @default.
- W2873418536 creator A5027026757 @default.
- W2873418536 date "2018-05-01" @default.
- W2873418536 modified "2023-09-24" @default.
- W2873418536 title "Online additive updates with FFT-IFFT operator on the GRU neural networks" @default.
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- W2873418536 doi "https://doi.org/10.1109/siu.2018.8404456" @default.
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