Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning...
—Social and communication networks across the world generate vast amounts of graph-like data each day. The modeling and prediction of how these communication structures evolve ca...
A prototype system for the automatic evolution of biomimetic structures using structural automata is described and its utility for generating digital sculpture is demonstrated. Sc...
Brower Hatcher, Karl Aspelund, Andrew R. Willis, J...
Abstract. Modeling the statistical structure of natural images is interesting for reasons related to neuroscience as well as engineering. Currently, this modeling relies heavily on...
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a ...
David Maxwell Chickering, Christopher Meek, David ...