Shuffling Recurrent Neural Networks
Abstract
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 β(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. We share our implementation at https://github.com/rotmanmi/SRNN.
Cite
Text
Rotman and Wolf. "Shuffling Recurrent Neural Networks." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I11.17136Markdown
[Rotman and Wolf. "Shuffling Recurrent Neural Networks." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/rotman2021aaai-shuffling/) doi:10.1609/AAAI.V35I11.17136BibTeX
@inproceedings{rotman2021aaai-shuffling,
title = {{Shuffling Recurrent Neural Networks}},
author = {Rotman, Michael and Wolf, Lior},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {9428-9435},
doi = {10.1609/AAAI.V35I11.17136},
url = {https://mlanthology.org/aaai/2021/rotman2021aaai-shuffling/}
}