Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al.^{[1]} The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features,^{[2]} but lacks a context vector or output gate, resulting in fewer parameters than LSTM.^{[3]}
GRU's performance on certain tasks of polyphonic music modeling, speech signal modeling and natural language processing was found to be similar to that of LSTM.^{[4]}^{[5]} GRUs showed that gating is indeed helpful in general, and Bengio's team came to no concrete conclusion on which of the two gating units was better.^{[6]}^{[7]}

Architecture

There are several variations on the full gated unit, with gating done using the previous hidden state and the bias in various combinations, and a simplified form called minimal gated unit.^{[8]}

The operator $\odot$ denotes the Hadamard product in the following.

Fully gated unit

Initially, for $t=0$, the output vector is $h_{0}=0$.

$W\in \mathbb {R} ^{d\times e))$, $U\in \mathbb {R} ^{e\times e))$ and $b\in \mathbb {R} ^{e))$: parameter matrices and vector which need to be learned during training

The minimal gated unit (MGU) is similar to the fully gated unit, except the update and reset gate vector is merged into a forget gate. This also implies that the equation for the output vector must be changed:^{[10]}

The light gated recurrent unit (LiGRU)^{[4]} removes the reset gate altogether, replaces tanh with the ReLU activation, and applies batch normalization (BN):

LiGRU has been studied from a Bayesian perspective.^{[11]} This analysis yielded a variant called light Bayesian recurrent unit (LiBRU), which showed slight improvements over the LiGRU on speech recognition tasks.

References

^Cho, Kyunghyun; van Merrienboer, Bart; Bahdanau, DZmitry; Bougares, Fethi; Schwenk, Holger; Bengio, Yoshua (2014). "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation". Association for Computational Linguistics. arXiv:1406.1078.

^Dey, Rahul; Salem, Fathi M. (2017-01-20). "Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks". arXiv:1701.05923 [cs.NE].

^Heck, Joel; Salem, Fathi M. (2017-01-12). "Simplified Minimal Gated Unit Variations for Recurrent Neural Networks". arXiv:1701.03452 [cs.NE].

^Bittar, Alexandre; Garner, Philip N. (May 2021). "A Bayesian Interpretation of the Light Gated Recurrent Unit". ICASSP 2021. 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Toronto, ON, Canada: IEEE. pp. 2965–2969. 10.1109/ICASSP39728.2021.9414259.