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In this paper, we propose a novel neural network structure, namely \emph{feedforward sequential memory networks (FSMN)}, to model long-term dependency in time series without using recurrent feedback.
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Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition
Abdel-Hamid, O., Mohamed, A., Jiang, H., and Penn, G · 2012
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Generating sequences with recurrent neural networks
Graves, A · 2013
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Graves, A., Mohamed, A., and Hinton, G. E · 2013
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Pascanu, R., Gulcehre, C., Cho, K., and Bengio, Y · 2013
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Weston, J., Chopra, S., and A., Bordes · 2014
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Training deep bidirectional LSTM acoustic model for LVCSR by a context-sensitive-chunk BPTT approach
Chen, K., Yan, Z. J., and Huo, Q · 2015
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Character-aware neural language models
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. E · 2015
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Zhang, S. and Jiang, H · 2015
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