Methods › Sequential › Recurrent Neural Networks › CGRU

Convolutional GRU

CGRU

2 papers tagged archive 2025-07-28

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

A Convolutional Gated Recurrent Unit is a type of GRU that combines GRUs with the convolution operation. The update rule for input xₜ and the previous output hₜ₋₁ is given by the following:

r = σ(Wᵣ ⋆ₙ[hₜ₋₁;xₜ] + bᵣ)

u = σ(Wᵤ ⋆ₙ[hₜ₋₁;xₜ] + bᵤ )

c = ρ(W_c ⋆ₙ[xₜ; r ⊙hₜ₋₁] + b_c )

hₜ = u ⊙hₜ₋₁ + (1-u) ⊙c

In these equations σ and ρ are the elementwise sigmoid and ReLU functions respectively and the ⋆ₙ represents a convolution with a kernel of size n ×n. Brackets are used to represent a feature concatenation.

Source: Delving Deeper into Convolutional Networks for Learning...

Papers archive 2025-07-28

2 shown of 2, 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

7 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
3D Character Animation From A Single Photo1
Action Recognition1
Decoder1
Temporal Action Localization1
Video Captioning1
Video Generation1
Video Prediction1

Usage over time archive 2025-07-28

Papers per year tagged with CGRU: 2015 to 2019, peak 1 1 0 2015: 1 paper 2015 2016: 0 papers 2016 2017: 0 papers 2017 2018: 0 papers 2018 2019: 1 paper 2019
Papers per year the archive tags with this method, by the paper's archive date (2 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 Networks

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