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Approximate Inference By Kullback-Leibler Tensor Belief Propagation

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IEEE ICASSP 2020 Virtual Conference May 2020

Probabilistic programming provides a structured approach to signal processing algorithm design. The design task is formulated as a generative model, and the algorithm is derived through automatic inference. Efficient inference is a major challenge; e.g.,

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Approximate Inference By Kullback-Leibler Tensor Belief Propagation

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Probabilistic programming provides a structured approach to signal processing algorithm design. The design task is formulated as a generative model, and the algorithm is derived through automatic inference. Efficient inference is a major challenge; e.g.,