We present a variational Bayesian framework for performing inference, density estimation and model selection in a special class of graphical models--Hidden Markov Random Fields (H...
Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang
In macromodeling-based power estimation, circuit macromodels are created from simulations of synthetic input vector sequences. Fast generation of these sequences with all possible...
In this paper, we propose a new type of image feature, which consists of patterns of colors and intensities that capture the latent associations among images and primitive feature...
Abstract. Database modeling is based on the assumption of a high regularity of its application areas, an assumption which applies to both the structure of data and the behavior of ...
Hans-Werner Sehring, Sebastian Bossung, Joachim W....
We describe how to teach deformable models to maximize image segmentation correctness based on user-specified criteria, and we present a method for evaluating which criteria work ...