  Probabilistic circuits (PCs) have become the de-facto standard for learning
and inference in probabilistic modeling. We introduce Sum-Product-Attention
Networks (SPAN), a new generative model that integrates probabilistic circuits
with Transformers. SPAN uses self-attention to select the most relevant parts
of a probabilistic circuit, here sum-product networks, to improve the modeling
capability of the underlying sum-product network. We show that while modeling,
SPAN focuses on a specific set of independent assumptions in every product
layer of the sum-product network. Our empirical evaluations show that SPAN
outperforms state-of-the-art probabilistic generative models on various
benchmark data sets as well is an efficient generative image model.
