Mattia Desana

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Sum-Product Networks (SPNs) are a recent class of probabilistic models which encode very large mixtures compactly by exploiting efficient reuse of computation in inference. Crucially, in SPNs the cost of inference scales linearly with the number of edges E but the encoded mixture size C can be exponentially larger than E. In this paper we obtain an(More)
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