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arXiv · 1512.08756

Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems

Colin Raffel·Daniel P. W. Ellis
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Abstract

We propose a simplified model of attention which is applicable to feed-forward neural networks and demonstrate that the resulting model can solve the synthetic "addition" and "multiplication" long-term memory problems for sequence lengths which are both longer and more widely varying than the best published results for these tasks.

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Colin Raffel, Daniel P. W. Ellis. 2015-12-29. Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems. https://arxiv.org/abs/1512.08756

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