In Genetic algorithms it is not easy to evaluate the confidence level in whether a GA run may have missed a complete area of good points, and whether the global optimum was found....
We introduce a method that enables scalable image search for learned metrics. Given pairwise similarity and dissimilarity constraints between some images, we learn a Mahalanobis d...
We propose a new kernel function for attributed molecular graphs, which is based on the idea of computing an optimal assignment from the atoms of one molecule to those of another ...
A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process c...
In this paper we introduce a novel Riemannian framework for shape analysis of parameterized surfaces. We derive a distance function between any two surfaces that is invariant to r...
Sebastian Kurtek, Eric Klassen, Anuj Srivastava, Z...