In this paper we present VOCUS: a robust computational attention system for goal-directed search. A standard bottom-up architecture is extended by a top-down component, enabling th...
The complexity, variation, and change of human languages makes evident the importance of representation and learning in the acquisition and evolution of language. For example, anal...
Yoosook Lee, Travis C. Collier, Gregory M. Kobele,...
We study the extension of applicability of system-level testing techniques to the construction of a consistent model of (legacy) systems under test, which are seen as black boxes....
Learning Bayesian networks from data has been studied extensively in the evolutionary algorithm communities [Larranaga96, Wong99]. We have previously explored extending some of the...
This paper studies the use of discrete-time recurrent neural networks for predicting the next symbol in a sequence. The focus is on online prediction, a task much harder than the c...