# Deep Gate Recurrent Neural Network

arXiv: https://arxiv.org/abs/1604.02910 · HTML: https://mlsanity.com/paper/1604.02910/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2016.

## Built on by (most cited first, 6 of 6)

- [AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning](https://mlsanity.com/paper/2310.15719/) (2023)
- [LSTM-Based Text Generation: A Study on Historical Datasets](https://mlsanity.com/paper/2403.07087/) (2024)
- [PerfMamba: Performance Analysis and Pruning of Selective State Space Models](https://mlsanity.com/paper/2511.22849/) (2025)
- [Differential Evolution Algorithm based Hyper-Parameters Selection of Transformer Neural Network Model for Load Forecasting](https://mlsanity.com/paper/2307.15299/) (2023)
- [Predicting Parkinson's disease evolution using deep learning](https://mlsanity.com/paper/2312.17290/) (2023)
- [Implementing a GRU Neural Network for Flood Prediction in Ashland City, Tennessee](https://mlsanity.com/paper/2405.10375/) (2024)

## Abstract (arXiv)

This paper introduces two recurrent neural network structures called Simple Gated Unit (SGU) and Deep Simple Gated Unit (DSGU), which are general structures for learning long term dependencies. Compared to traditional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), both structures require fewer parameters and less computation time in sequence classification tasks. Unlike GRU and LSTM, which require more than one gates to control information flow in the network, SGU and DSGU only use one multiplicative gate to control the flow of information. We show that this difference can accelerate the learning speed in tasks that require long dependency information. We also show that DSGU is more numerically stable than SGU. In addition, we also propose a standard way of representing inner structure of RNN called RNN Conventional Graph (RCG), which helps analyzing the relationship between input units and hidden units of RNN.