Ensemble learning is a variational Bayesian method in which an intractable distribution is approximated by a lower-bound. Ensemble learning results in models with better generaliz...
In this paper, we propose a distributed compression approach for multi-view images, where each camera efficiently encodes its visual information locally without requiring any col...
We consider the problem of increasing the availability of shared data in peer-to-peer (P2P) systems so that users can access any content, regardless of the current subset of onlin...
Francisco Matias Cuenca-Acuna, Richard P. Martin, ...
—In a sensor network, data routing is tightly coupled to the needs of a sensing task, and hence the application semantics. This paper introduces the novel idea of information-dir...
Recent work on information integration has yielded novel and efficient solutions for gathering data from the World Wide Web. However, there has been little attention given to the ...
Greg Barish, Dan DiPasquo, Craig A. Knoblock, Stev...