What happens to the optimal interpretation of noisy data when there exists more than one equally plausible interpretation of the data? In a Bayesian model-learning framework the a...
A hybrid learning neuro-fuzzy system with asymmetric fuzzy sets (HLNFS-A) is proposed in this paper. The learning methods of random optimization (RO) and least square estimation (...
We describe a generative model for graph edges under specific degree distributions which admits an exact and efficient inference method for recovering the most likely structure. T...
Recent work has shown that one can learn the structure of Gaussian Graphical Models by imposing an L1 penalty on the precision matrix, and then using efficient convex optimization...
Learning good image priors is of utmost importance for the study of vision, computer vision and image processing applications. Learning priors and optimizing over whole images can...