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Mixture model network

MoNet

25 papers tagged archive 2025-07-28

Introduced by Federico Monti et al. in Geometric deep learning on graphs and manifolds using mixture model CNNs

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

Mixture model network (MoNet) is a general framework allowing to design convolutional deep architectures on non-Euclidean domains such as graphs and manifolds.

Image and description from: Geometric deep learning on graphs and manifolds using mixture model CNNs

PaperSource

Papers archive 2025-07-28

25 shown of 25, 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 58 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
Representation Learning4
Object3
Optical Flow Estimation3
Segmentation3
Semantic Segmentation3
Image Generation2
Object Detection2
Object Discovery2
Recommendation Systems2
Unsupervised Object Segmentation2
object-detection2
Action Unit Detection1
Astronomy1
Attribute1
Autonomous Driving1
Autonomous Navigation1
BIG-bench Machine Learning1
Classification1
Crowd Counting1
Data Augmentation1

Usage over time archive 2025-07-28

Papers per year tagged with MoNet: 2016 to 2024, peak 4 4 0 2016: 1 paper 2016 2017: 0 papers 2017 2018: 4 papers 2018 2019: 3 papers 2019 2020: 4 papers 2020 2021: 4 papers 2021 2022: 2 papers 2022 2023: 3 papers 2023 2024: 4 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (25 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

Graph Models

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