Projections of model spaces for latent graph inference
Graph Neural Networks leverage the connectivity structure of graphs as an inductive bias. Latent graph inference focuses on learning an adequate graph structure to diffuse information on. In this work we employ stereographic projections of the hyperbolic and spherical model spaces, as well as produc...
Main Authors: | Posner, H, Arroyo, A, Sáez De Ocáriz Borde, H |
---|---|
Formato: | Conference item |
Idioma: | English |
Publicado: |
OpenReview
2023
|
Títulos similares
-
Neural latent geometry search: product manifold inference via Gromov-Hausdorff-informed Bayesian optimization
por: Sáez de Ocáriz Borde, H, et al.
Publicado: (2024) -
GENESIS: generative scene inference and sampling of object-centric latent representations
por: Engelcke, M, et al.
Publicado: (2020) -
DreamUp3D: object-centric generative models for single-view 3D scene understanding and real-to-sim transfer
por: Wu, Y, et al.
Publicado: (2024) -
Efficient state-space inference of periodic latent force models
por: Reece, S, et al.
Publicado: (2014) -
Latent source models for nonparametric inference
por: Chen, George H
Publicado: (2015)