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We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Compo...
We introduce a novel framework for simultaneous structure and parameter learning in hidden-variable conditional probability models, based on an entropic prior and a solution for i...
In recent years, theChaos Project at theUniversityofWashingtonhas analyzed and simulated a dozen routing algorithms. Three new routing algorithms have been invented; of these, the...
Neil R. McKenzie, Kevin Bolding, Carl Ebeling, Law...
We approach the problem of stylistic motion synthesis by learning motion patterns from a highly varied set of motion capture sequences. Each sequence may have a distinct choreogra...
This paper addresses several aspects related to the distribution of content. The first aim is to provide an overview of the Universal Multimedia Access (UMA) concept. The primary...