# Sliced Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1807.02291 · HTML: https://mlsanity.com/paper/1807.02291/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2018.

## Built on by (most cited first, 7 of 7)

- [T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification](https://mlsanity.com/paper/2103.04264/) (2021)
- [Real-world Video Deblurring: A Benchmark Dataset and An Efficient Recurrent Neural Network](https://mlsanity.com/paper/2106.16028/) (2021)
- [Recursion in Recursion: Two-Level Nested Recursion for Length Generalization with Scalability](https://mlsanity.com/paper/2311.04449/) (2023)
- [Estimator: An Effective and Scalable Framework for Transportation Mode Classification over Trajectories](https://mlsanity.com/paper/2212.05502/) (2022)
- [PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting](https://mlsanity.com/paper/2409.17703/) (2024)
- [FV-MgNet: Fully Connected V-cycle MgNet for Interpretable Time Series Forecasting](https://mlsanity.com/paper/2302.00962/) (2023)
- [Length Generalization with Log-Depth Recurrent Units](https://mlsanity.com/paper/2605.26035/) (2026)

## Abstract (arXiv)

Recurrent neural networks have achieved great success in many NLP tasks. However, they have difficulty in parallelization because of the recurrent structure, so it takes much time to train RNNs. In this paper, we introduce sliced recurrent neural networks (SRNNs), which could be parallelized by slicing the sequences into many subsequences. SRNNs have the ability to obtain high-level information through multiple layers with few extra parameters. We prove that the standard RNN is a special case of the SRNN when we use linear activation functions. Without changing the recurrent units, SRNNs are 136 times as fast as standard RNNs and could be even faster when we train longer sequences. Experiments on six largescale sentiment analysis datasets show that SRNNs achieve better performance than standard RNNs.