# Sequence Modeling using Gated Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1501.00299 · HTML: https://mlsanity.com/paper/1501.00299/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2015.

## Built on by (most cited first, 1 of 1)

- [A novel DL approach to PE malware detection: exploring Glove vectorization, MCC_RCNN and feature fusion](https://mlsanity.com/paper/2101.08969/) (2021)

## Abstract (arXiv)

In this paper, we have used Recurrent Neural Networks to capture and model human motion data and generate motions by prediction of the next immediate data point at each time-step. Our RNN is armed with recently proposed Gated Recurrent Units which has shown promising results in some sequence modeling problems such as Machine Translation and Speech Synthesis. We demonstrate that this model is able to capture long-term dependencies in data and generate realistic motions.