AI Summary: Introduces Pointer Networks, a novel sequence-to-sequence architecture that uses attention mechanisms to directly 'point' to elements in the input sequence, enabling neural networks to solve complex combinatorial optimization problems.
We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. Such problems cannot be trivially solved by sequence-to-sequence models since the size of the output dictionary depends on the length of the input sequence. We propose the 'Pointer Network' (Ptr-Net), which uses an attention mechanism to create pointers to elements of the input sequence, rather than blending them into a fixed-size context vector. We demonstrate that Ptr-Nets can learn to solve three challenging geometric combinatorial optimization problems—finding planar convex hulls, computing Delaunay triangulations, and solving the planar Travelling Salesman Problem—directly from data.
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