arXiv (All)
open access
How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?
Graphs are a powerful data structure for representing relational data and are widely used to describe complex real-world systems. Probabilistic Graphical Models (PGMs) and Graph Neural Networks (GNNs) can both leverage graph-structured data, but their inherent functioning is different. The question
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Triply-Scalable Equivariant Gaussian Process Modeling#1035055
- Teacher Geometry Shapes Learnability in Teacher-Student Networks#987328
- The Global Neuronal Workspace as a broadcasting network#114471
- Predictively Oriented Gaussian Process Posteriors#1187879
- Resource-Efficient Distributed Recursive Gaussian Processes#1063190
Record · ID 1201891
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive