Differentiable Nonparametric Belief Propagation

Understand

We present a differentiable approach to learn the probabilistic factors used for inference by a nonparametric belief propagation algorithm.

• Existing nonparametric belief propagation methods rely on domain-specific features encoded in the probabilistic factors of a graphical model.
• In this work, we replace each crafted factor with a differentiable neural network enabling the factors to be learned using an efficient optimization routine from labeled data.
• By combining differentiable neural networks with an efficient belief propagation algorithm, our method learns to maintain a set of marginal posterior samples using end-to-end training.