In this work we take a novel view of nonlinear manifold learning. Usually, manifold learning is formulated in terms of finding an embedding or `unrolling' of a manifold into ...
A variety of compilers, static analyses, and testing frameworks rely heavily on path frequency information. Uses for such information range from optimizing transformations to bug ...
We propose a new global registration method for estimating the cardiac displacement field in 2D sequences of ultrasound images of the heart. The basic idea is to select a referenc...
We propose a simple yet potentially very effective way of visualizing trained support vector machines. Nomograms are an established model visualization technique that can graphica...
Aleks Jakulin, Martin Mozina, Janez Demsar, Ivan B...
A reliable and unobtrusive measurement of working memory load could be used to evaluate the efficacy of interfaces and to provide real-time user-state information to adaptive syst...
David B. Grimes, Desney S. Tan, Scott E. Hudson, P...