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Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks

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arxiv 1701.05923 v1 pith:64GN7222 submitted 2017-01-20 cs.NE stat.ML

Rahul Dey , Fathi M. Salem This is my paper
classification cs.NEstat.ML
keywords recurrentgatedmodelsnetworksneuralreducingthreeunit
verification ladder T0 review T1 audit T2 compute T3 formal
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The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNN) by reducing parameters in the update and reset gates. We evaluate the three variant GRU models on MNIST and IMDB datasets and show that these GRU-RNN variant models perform as well as the original GRU RNN model while reducing the computational expense.

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Cited by 2 Pith papers

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