Sum-product networks are a relatively new and increasingly popular family of probabilistic
graphical models that allow for marginal inference with polynomial effort. They have been …

Recent research highlights the significance of incorporating density modeling into clustering
procedures. While the Sum-Product Networks' ability to compactly represent mixture models …

Abstract Sum-Product Networks (SPN) are deep probabilistic models that have shown to
achieve state-of-the-art performance in several machine learning tasks. As with many other …

Classifier chains are an effective technique for modeling label dependencies in multi-label
classification. However, the method requires a fixed, static order of the labels. While in …

Classifier chains are an effective technique for modeling label dependencies in multi-label
classification. However, the method requires a fixed, static order of the labels. While in …

Abstract Sum-Product Networks (SPN) are deep probabilistic models with demonstrated
excellent performance in several machine learning tasks. As with many other probabilistic …

A Probabilistic Circuit (PC) is an expressive generative model that encodes a probability
distribution through an structure of weighted sums, products and univariate or multivariate …

Abstract MADEIRA, Tiago. Finding Maxima of Gaussian Sum-Product Networks. Thesis
(Masters). Institute of Mathematics and Statistics, University of Sao Paulo, Sao Paulo, 2023 …

Probabilistic circuits (PCs) are a family of tractable probabilistic models capable of
answering a wide range of queries exactly and in polytime. While inference is usually …

• Sum-Product Networks are deep generative probabilistic models with state-of-the-art
performance in several machine learning tasks.• Models learned from data can produce …