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The Ethereal
Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems
December 29, 2015 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Colin Raffel, Daniel P. W. Ellis
arXiv ID
1512.08756
Category
cs.LG: Machine Learning
Cross-listed
cs.NE
Citations
318
Venue
arXiv.org
Last Checked
3 months ago
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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