We consider the problem of learning to map between two vector spaces given pairs of matching vectors, one from each space. This problem naturally arises in numerous vision problem...
Segmentation is a low-level vision cue often deployed by stereo algorithms to assume that disparity within superpixels varies smoothly. In this paper, we show that constraining, o...
Abstract. Many large-scale optimization problems rely on graph theoretic solutions; yet high-performance computing has traditionally focused on regular applications with high degre...
Estimation of distribution algorithms (EDAs) are widely used in stochastic optimization. Impressive experimental results have been reported in the literature. However, little work ...
Abstract. Phylogeny reconstruction from molecular data poses complex optimization problems: almost all optimization models are NP-hard and thus computationally intractable. Yet app...