Add to ORKGSum-Product Networks (SPNs) were recently proposed as a new class of probabilistic graphical models that guarantee tractable inference, even on models with high-treewidth. In this paper, we propose a new extension to SPNs, called Decision Sum-Product-Max Networks (Decision-SPMNs), that makes SPNs suitable for discrete multi-stage decision problems. We present an algorithm that solves Decision-SPMNs in a time that is linear in the size of the network. We also present algorithms to learn the parameters of the network from data
Probabilistic graphical models have been successfully applied to a wide variety of fields such as co...
Sum-product networks are a relatively new and increasingly popular family of probabilistic graphical...
Sum-product networks are a relatively new and increasingly popular class of (precise) probabilistic ...
© 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. ...
Sum-product networks (SPNs) are a class of probabilistic graphical models that allow tractable margi...
Sum-product networks (SPNs) are a recently-proposed deep architecture that guarantees tractable infe...
Sum-Product Networks (SPNs) are probabilistic inference machines that admit exact inference in linea...
Sum-Product Networks (SPNs) are deep tractable probabilistic models by which several kinds of infere...
Sum-product networks (SPNs) are a recently developed class of deep probabilistic models where infere...
In this paper, we establish some theoretical con-nections between Sum-Product Networks (SPNs) and Ba...
The need for feasible inference in Probabilistic Graphical Models (PGMs) has lead to tractable model...
Sum-product networks allow to model complex variable interactions while still granting efficient inf...
Discriminatively-trained probabilistic models often outperform their generative counterparts on chal...
Sum-product networks (SPNs) are expressive probabilistic models with a rich set of exact and efficie...
Sum-product networks (SPNs) are a promising avenue for probabilistic modeling and have been successf...
Probabilistic graphical models have been successfully applied to a wide variety of fields such as co...
Sum-product networks are a relatively new and increasingly popular family of probabilistic graphical...
Sum-product networks are a relatively new and increasingly popular class of (precise) probabilistic ...
© 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. ...
Sum-product networks (SPNs) are a class of probabilistic graphical models that allow tractable margi...
Sum-product networks (SPNs) are a recently-proposed deep architecture that guarantees tractable infe...
Sum-Product Networks (SPNs) are probabilistic inference machines that admit exact inference in linea...
Sum-Product Networks (SPNs) are deep tractable probabilistic models by which several kinds of infere...
Sum-product networks (SPNs) are a recently developed class of deep probabilistic models where infere...
In this paper, we establish some theoretical con-nections between Sum-Product Networks (SPNs) and Ba...
The need for feasible inference in Probabilistic Graphical Models (PGMs) has lead to tractable model...
Sum-product networks allow to model complex variable interactions while still granting efficient inf...
Discriminatively-trained probabilistic models often outperform their generative counterparts on chal...
Sum-product networks (SPNs) are expressive probabilistic models with a rich set of exact and efficie...
Sum-product networks (SPNs) are a promising avenue for probabilistic modeling and have been successf...
Probabilistic graphical models have been successfully applied to a wide variety of fields such as co...
Sum-product networks are a relatively new and increasingly popular family of probabilistic graphical...
Sum-product networks are a relatively new and increasingly popular class of (precise) probabilistic ...