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

1 Pith paper cite this work. Polarity classification is still indexing.

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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.

fields

cs.IR 1

years

2019 1

verdicts

UNVERDICTED 1

representative citing papers

An Attention Mechanism for Musical Instrument Recognition

cs.IR · 2019-07-09 · unverdicted · novelty 5.0

An attention model improves multi-label instrument recognition accuracy on the weakly labeled OpenMIC dataset compared to baseline, RNN, and fully connected networks across 20 instruments.

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  • An Attention Mechanism for Musical Instrument Recognition cs.IR · 2019-07-09 · unverdicted · none · ref 41 · internal anchor

    An attention model improves multi-label instrument recognition accuracy on the weakly labeled OpenMIC dataset compared to baseline, RNN, and fully connected networks across 20 instruments.