# Shuffling Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2007.07324 · HTML: https://mlsanity.com/paper/2007.07324/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 11 of 11)

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- [Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach](https://mlsanity.com/paper/2406.13201/) (2024)
- [Random orthogonal additive filters: a solution to the vanishing/exploding gradient of deep neural networks](https://mlsanity.com/paper/2210.01245/) (2022)
- [Lempel-Ziv Networks](https://mlsanity.com/paper/2211.13250/) (2022)
- [Dynamic Analysis and an Eigen Initializer for Recurrent Neural Networks](https://mlsanity.com/paper/2307.15679/) (2023)
- [IT Intrusion Detection Using Statistical Learning and Testbed Measurements](https://mlsanity.com/paper/2402.13081/) (2024)
- [CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship](https://mlsanity.com/paper/2408.15620/) (2024)
- [A deep real options policy for sequential service region design and timing](https://mlsanity.com/paper/2212.14800/) (2022)
- [A Unifying Framework of Attention-based Neural Load Forecasting](https://mlsanity.com/paper/2305.05082/) (2023)

## Abstract (arXiv)

We propose a novel recurrent neural network model, where the hidden state ht is obtained by permuting the vector elements of the previous hidden state ht-1 and adding the output of a learned function b(xt) of the input xt at time t. In our model, the prediction is given by a second learned function, which is applied to the hidden state s(ht). The method is easy to implement, extremely efficient, and does not suffer from vanishing nor exploding gradients. In an extensive set of experiments, the method shows competitive results, in comparison to the leading literature baselines.