• Pointer Networks

  • Nov 2 2024
  • Length: 13 mins
  • Podcast

Pointer Networks

Listen for free

View show details

  • Summary

  • 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
    Show More Show Less
Show More Show Less
activate_Holiday_promo_in_buybox_DT_T2

What listeners say about Pointer Networks

Average customer ratings

Reviews - Please select the tabs below to change the source of reviews.

Audible.co.uk reviews

Amazon reviews
No Reviews are Available