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Found 3 papers, 2 papers with code
$α$ Belief Propagation for Approximate Inference
To gain a better understanding of BP in general graphs, we derive an interpretable belief propagation algorithm that is motivated by minimization of a localized $\alpha$-divergence.
Neural Network based Explicit Mixture Models and Expectation-maximization based Learning
A single latent variable is used as the common input to all the neural networks.
Entropy-regularized Optimal Transport Generative Models
We investigate the use of entropy-regularized optimal transport (EOT) cost in developing generative models to learn implicit distributions.
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