Recent neural network sequence models with softmax classifiers have achieved their best
language modeling performance only with very large hidden states and large vocabularies …

We describe a simple neural language model that relies only on character-level inputs.
Predictions are still made at the word-level. Our model employs a convolutional neural …

While all kinds of mixed data---from personal data, over panel and scientific data, to public
and commercial data---are collected and stored, building probabilistic graphical models for …

One of the central themes in Sum-Product networks (SPNs) is the interpretation of sum
nodes as marginalized latent variables (LVs). This interpretation yields an increased …

Sum-product networks (SPNs) are a promising avenue for probabilistic modeling and have
been successfully applied to various tasks. However, some theoretic properties about SPNs …

The need for feasible inference in Probabilistic Graphical Models (PGMs) has lead to
tractable models like Sum-Product Networks (SPNs). Their highly expressive power and …

Sum–product networks (SPNs) in deep probabilistic models have made great progress in
computer vision, robotics, neuro-symbolic artificial intelligence, natural language …

In this paper, we establish some theoretical connections between Sum-Product Networks
(SPNs) and Bayesian Networks (BNs). We prove that every SPN can be converted into a BN …

A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed
graph, in which terminal nodes represent probability distributions and non-terminal nodes …

Abstract The recent Attend-Infer-Repeat (AIR) framework marks a milestone in structured
probabilistic modeling, as it tackles the challenging problem of unsupervised scene …