# Minimal Gated Unit for Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1603.09420 · HTML: https://mlsanity.com/paper/1603.09420/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2016.

## Built on by (most cited first, 30 of 40)

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- [Gates Are Not What You Need in RNNs](https://mlsanity.com/paper/2108.00527/) (2021)
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- [Minion Gated Recurrent Unit for Continual Learning](https://mlsanity.com/paper/2503.06175/) (2025)
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## Abstract (arXiv)

Recently recurrent neural networks (RNN) has been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN is a difficult task, partly because there are many competing and complex hidden units (such as LSTM and GRU). We propose a gated unit for RNN, named as Minimal Gated Unit (MGU), since it only contains one gate, which is a minimal design among all gated hidden units. The design of MGU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MGU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN's applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU's properties theoretically and empirically.