Papers with light recurrent unit

1 papers
Simple Recurrent Units for Highly Parallelizable Recurrence (D18-1)

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Challenge: recurrent neural networks scale poorly due to the intrinsic difficulty in parallelizing their state computations.
Approach: They propose a simple recurrent unit that provides expressive recurrence and allows highly parallel implementation.
Outcome: The proposed model achieves 5—9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets and delivers stronger results than LS and convolutional models.

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