Gated recurrent unit
lastmod 2018-10-24created 2018-10-243 edits
Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. Their performance on polyphonic music modeling and speech signal modeling was found to be similar to that of long short-term memory (LSTM). However, GRUs have been shown to exhibit better performance on smaller datasets.
They have fewer parameters than LSTM, as they lack an output gate.
GRU overview
The GRU is a cell structure that keeps the advantages of the LSTM while greatly reducing computational complexity. The GRU is similar to the LSTM in that it overcomes the Gradient Vanishing/Explosion problem, but it omits some of the gates. The GRU is broadly divided into two gates: the update gate and the reset gate.