This episode breaks down the Pointer Networks research paper, which proposes a novel neural network architecture called Pointer Networks (Ptr-Nets), designed to learn the probability of an output sequence based on an input sequence. Unlike traditional sequence-to-sequence models, Ptr-Nets are capable of handling variable-length output dictionaries, a crucial feature for addressing combinatorial optimisation problems where the output size depends on the input. The paper demonstrates the effectiveness of Ptr-Nets by applying them to three geometric problems: finding planar convex hulls, computing Delaunay triangulations, and solving the travelling salesman problem. The authors show that Ptr-Nets outperform existing methods and demonstrate that they can generalise to larger input sizes, even when trained on smaller datasets.

Audio : (Spotify) https://open.spotify.com/episode/3LEheJ4NnDHhXY7lQrZTuI?si=eIgSallCQiG_Bln4OOFazw

Paper: https://arxiv.org/abs/1506.03134v2