Methods › Sequential › Recurrent Neural Networks › Pointer Network

Pointer Network

105 papers tagged archive 2025-07-28

Introduced by Oriol Vinyals et al. in Pointer Networks

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Pointer Networks tackle problems where input and output data are sequential data, but can't be solved by seq2seq type models because discrete categories of output elements depend on the variable input size (and are not decided in advance).

A Pointer Network learns the conditional probability of an output sequence with elements that are discrete tokens corresponding to positions in an input sequence. They solve the problem of variable size output dictionaries using additive attention. But instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, Pointer Networks use attention as a pointer to select a member of the input sequence as the output.

Pointer-Nets can be used to learn approximate solutions to challenging geometric problems such as finding planar convex hulls, computing Delaunay triangulations, and the planar Travelling Salesman Problem.

PaperSource

Papers archive 2025-07-28

30 shown of 105, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 136 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Reinforcement Learning (RL)15
Sentence15
reinforcement-learning15
Combinatorial Optimization14
Deep Reinforcement Learning14
Reinforcement Learning12
Language Modelling8
Question Answering7
Starcraft II7
Decoder6
Language Modeling6
Starcraft6
Text Generation5
Traveling Salesman Problem5
Abstractive Text Summarization4
Dialogue State Tracking4
Diversity4
Document Summarization4
Knowledge Graphs4
Relation4

Usage over time archive 2025-07-28

Papers per year tagged with Pointer Network: 2015 to 2024, peak 28 28 0 2015: 1 paper 2015 2016: 3 papers 2016 2017: 1 paper 2017 2018: 6 papers 2018 2019: 21 papers 2019 2020: 22 papers 2020 2021: 28 papers 2021 2022: 10 papers 2022 2023: 10 papers 2023 2024: 3 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (105 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Recurrent Neural NetworksSequence To Sequence Models

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