Many domains in the real world are richly structured, containing a diverse set of objects, related to each other in a variety of ways. For example, a living cell contains a rich network of interacting genes, that come together to perform key functions.
A robot scan of a physical environment contains diverse objects such as people, vehicles, trees, or buildings, each of which might itself be a structured object. And a website contains a set of interlinked webpages, representing diverse kinds of entities. This talk describes a rich language based on probabilistic graphical models, which allows us to model domains such as these. We show how to learn such models from data generated from the domain, and how to use the learned model both to gain a better understanding of the principles underlying these domains, and to allow us to analyze a new data set from these domains in order to recognize the entities in it and the relationships between them. In particular, I will describe applications of this framework to various tasks, including: recognizing regulatory and protein interactions in a cell from diverse types of genomic data; segmenting and recognizing objects in robot laser range scan data; and identifying the set of entities in a structured website and the relationships between them.
More information: http://www.cs.ubc.ca/events/seminars/csicics.shtml
Thursday, September 22, 2005 - 16:00 to 17:30
Where: DMP 310 - 6245 Agronomy Rd, Vancouver, BC, V6T 1Z4