Gate-variants of Gated Recurrent Unit (GRU) neural networks

The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNNs) by retaining the structure and systematically 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. In this comparative study, we simply refer to the three variants as, respectively, GRU1, GRU2, and GRU3 RNNs.