  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.
