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Showing 1 to 1 of 1 for “"Inference, Probabilistic graphical models, Graph neural networks"”.
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Inference in Ising models by graph neural networks with structural features
Probabilistic graphical models (PGMs) are powerful frameworks for modeling interactions between random variables. The two major inference tasks on PGMs are marginal probability inference and maximum-a-posteriori (MAP) inference. Exact inference on PGMs is intractable, hence approximation …