Methods › Natural Language Processing › Language Models › Gated Convolution Network

Gated Convolution Network

3 papers tagged archive 2025-07-28

Introduced by Yann N. Dauphin et al. in Language Modeling with Gated Convolutional Networks

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

A Gated Convolutional Network is a type of language model that combines convolutional networks with a gating mechanism. Zero padding is used to ensure future context can not be seen. Gated convolutional layers can be stacked on top of other hierarchically. Model predictions are then obtained with an adaptive softmax layer.

PaperSource

Papers archive 2025-07-28

3 shown of 3, 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

15 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
Continual Learning1
Denoising1
Diagnostic1
Domain-IL Continual Learning1
Language Modeling1
Language Modelling1
Permuted-MNIST1
Reinforcement Learning1
Reinforcement Learning (RL)1
SSIM1
Sensitivity1
Sentence1
Split-CIFAR-101
Split-MNIST1
reinforcement-learning1

Usage over time archive 2025-07-28

Papers per year tagged with Gated Convolution Network: 2016 to 2024, peak 1 1 0 2016: 1 paper 2016 2017: 0 papers 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 0 papers 2020 2021: 1 paper 2021 2022: 0 papers 2022 2023: 0 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (3 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

Language Models

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