Online Structure Learning for Sum-Product Networks with Gaussian Leaves
This work addresses a specific problem in probabilistic graphical models for researchers and practitioners needing automated, scalable SPN construction.
The paper tackles the challenge of manually specifying valid sum-product networks (SPNs) by introducing the first online structure learning technique for continuous SPNs with Gaussian leaves, along with a new parameter learning method.
Sum-product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which inference is always tractable. Those properties follow from some conditions (i.e., completeness and decomposability) that must be respected by the structure of the network. As a result, it is not easy to specify a valid sum-product network by hand and therefore structure learning techniques are typically used in practice. This paper describes the first online structure learning technique for continuous SPNs with Gaussian leaves. We also introduce an accompanying new parameter learning technique.