• Corpus ID: 220525932

Shuffling Recurrent Neural Networks

@inproceedings{Rotman2021ShufflingRN,
  title={Shuffling Recurrent Neural Networks},
  author={Michael Rotman and Lior Wolf},
  booktitle={AAAI},
  year={2021}
}
We propose a novel recurrent neural network model, where the hidden state $h_t$ is obtained by permuting the vector elements of the previous hidden state $h_{t-1}$ and adding the output of a learned function $b(x_t)$ of the input $x_t$ at time $t$. In our model, the prediction is given by a second learned function, which is applied to the hidden state $s(h_t)$. The method is easy to implement, extremely efficient, and does not suffer from vanishing nor exploding gradients. In an extensive set… 

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