Differential equation and probability inspired graph neural networks for latent variable learning
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Abstract (arXiv)
Probabilistic theory and differential equation are powerful tools for the interpretability and guidance of the design of machine learning models, especially for illuminating the mathematical motivation of learning latent variable from observation. Subspace learning maps high-dimensional features on low-dimensional subspace to capture efficient representation. Graphs are widely applied for modeling latent variable learning problems, and graph neural networks implement deep learning architectures on graphs. Inspired by probabilistic theory and differential equations, this paper conducts notes and proposals about graph neural networks to solve subspace learning problems by variational inference and differential equation.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 71 references that are in the map. First posted on arXiv 28 Feb 2022.
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27 of 71 references.
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