Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structur...
Modeling the performance behavior of parallel applications to predict the execution times of the applications for larger problem sizes and number of processors has been an active ...
Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of, mainly binary, optimization problems. In ...
We introduce a robust probabilistic approach to modeling shape contours based on a lowdimensional, nonlinear latent variable model. In contrast to existing techniques that use obj...
Prior distributions are useful for robust low-level vision, and undirected models (e.g. Markov Random Fields) have become a central tool for this purpose. Though sometimes these p...