Learning Sum-Product Networks
In several real-world scenarios, decision making involves advanced reasoning under uncertainty, i.e., the ability to answer complex probabilistic queries. However, answering probabilistic queries exactly often becomes intractable in complex probabilistic models. Tractable probabilistic models (probabilistic circuits), such as Sum-Product Networks (SPNs), are a recent and quickly growing field, promising a remedy for this problem. In contrast to traditional probabilistic models, SPNs can represent highly complex variable dependencies, while at the same time guaranteeing that many inference scenarios have costs linear in their representation size. In this talk, I’ll give an introduction to SPNs, discuss parameter learning of such models and introduce principled structure learning of SPNs. Lastly, I’ll highlight a few recent success stories of SPNs, ranging from computer vision to non-linear nonparametric regression.
January 30, 2020 (Thursday)
Room 205, Building E, Karlovo nám. 13