Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sum-Product-Attention Networks: Leveraging Self-Attention in
  Probabilistic Circuits   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.

28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn