Sequence stacking using dual encoder Seq2Seq recurrent networks
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Sequence stacking using dual encoder Seq2Seq recurrent networks
A widely studied nonpolynomial (NP) hard problem lies in finding a route between the two nodes of a graph. Often metaheuristics algorithms such as $A^{*}$ are employed on graphs with a large number of nodes. Here, we propose a deep recurrent neural network architecture based on the Sequence2Sequence model, widely used, for instance in text translation. Particularly, we illustrate that utilising a context vector that has been learned from two different recurrent networks enables increased accuracies in learning the shortest route of a graph. Additionally, we show that one can boost the performance of the Seq2Seq network by smoothing the loss function using a homotopy continuation of the decoder’s loss function.
Sequence stacking using dual encoder Seq2Seq recurrent networks
by Alessandro Bay, Biswa Sengupta
https://arxiv.org/pdf/1710.04211v1.pdf
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