Advances in Learning Sum-Product Networks
Sum-product networks (SPNs) are a recently proposed tractable probabilistic model allowing exact and efficient inference. This thesis focuses on discussing new learning paradigms for SPNs as well as integrating SPNs with recent research in Bayesian nonparameterics. In particular, I focus on the following research questions:
- How to determine parameters of SPNs for semi-supervised learning tasks?
- How can the structure of SPNs be learned in a flexible and principled way?
- Can SPNs be used to efficiently represent complex Bayesian nonparameteric models?