MoNet mixture-model convolutions on graphs and manifolds

What changed

Defines pseudo-coordinate patch operators with learned Gaussian-mixture kernels, unifying several spatial graph/manifold convolution constructions.

Scientific scope

Defines pseudo-coordinate patch operators with learned Gaussian-mixture kernels, unifying several spatial graph/manifold convolution constructions. First two authors share contribution. Inherits graph/manifold convolution precursors; performance and transfer depend on chosen pseudo-coordinates and tasks, not arbitrary topology/geometry invariance or universal cross-domain accuracy. Generic vision/graph architecture only.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. First two authors share contribution. Inherits graph/manifold convolution precursors; performance and transfer depend on chosen pseudo-coordinates and tasks, not arbitrary topology/geometry invariance or universal cross-domain accuracy. Generic vision/graph architecture only.

Contributors

• Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

• Geometric deep learning on graphs and manifolds using mixture model CNNs

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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