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How to Construct Deep Recurrent Neural Networks
[article]
2014
arXiv
pre-print
In this paper, we explore different ways to extend a recurrent neural network (RNN) to a deep RNN. We start by arguing that the concept of depth in an RNN is not as clear as it is in feedforward neural networks. By carefully analyzing and understanding the architecture of an RNN, however, we find three points of an RNN which may be made deeper; (1) input-to-hidden function, (2) hidden-to-hidden transition and (3) hidden-to-output function. Based on this observation, we propose two novel
arXiv:1312.6026v5
fatcat:dkmi2dyijncjlhnqlpay3klnrm