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The paper evaluates three variants of the Gated Recurrent Unit (GRU) inrecurrent neural networks (RNN) by reducing parameters in the update and resetgates. We evaluate the three variant GRU models on MNIST and IMDB datasets andshow that these GRU-RNN variant models perform as well as the original GRU RNNmodel while reducing the computational expense.

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