Recent Advances in Recurrent Neural Networks
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Recent Advances in Recurrent Neural Networks
Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and timeseries data. The RNNs have a stack of nonlinear units where at least one connection between units forms a directed cycle. A welltrained RNN can model any dynamical system; however, training RNNs is mostly plagued by issues in learning longterm dependencies. In this paper, we present a survey on RNNs and several new advances for newcomers and professionals in the field. The fundamentals and recent advances are explained and the research challenges are introduced.
Recent Advances in Recurrent Neural Networks
by Hojjat Salehinejad, Julianne Baarbe, Sharan Sankar, Joseph Barfett, Errol Colak, Shahrokh Valaee
https://arxiv.org/pdf/1801.01078v2.pdf
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