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.

翻译：概率电路(PCs)已成为概率模型中学习和推断的脱法标准。我们引入了总产量网络(SPAN),这是一个将概率电路与变异器结合的新型基因模型。SPAN利用自我意识选择概率电路中最相关的部分,即这里的合成产品网络,以提高基本总产品网络的建模能力。我们表明,在建模的同时,SPAN侧重于总产品网络中每个产品层的一套具体的独立假设。我们的经验评估表明,SPAN在各种基准数据集上,比最新最先进的概率模型要强,也是一个高效的基因化图像模型。