  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.
