[1512.08756] Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems
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Computer Science > Machine Learning

arXiv:1512.08756 (cs)
[Submitted on 29 Dec 2015 (v1), last revised 20 Sep 2016 (this version, v5)]

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

Authors: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.
Subjects: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:1512.08756 [cs.LG]
  (or arXiv:1512.08756v5 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.1512.08756

Submission history

From: Colin Raffel [view email]
[v1] Tue, 29 Dec 2015 19:03:43 UTC (35 KB)
[v2] Thu, 31 Dec 2015 17:52:46 UTC (35 KB)
[v3] Fri, 8 Jan 2016 01:52:06 UTC (36 KB)
[v4] Mon, 28 Mar 2016 02:41:21 UTC (102 KB)
[v5] Tue, 20 Sep 2016 05:02:22 UTC (37 KB)
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