Nonparametric Bayesian mixture models, in particular Dirichlet process (DP) mixture models, have shown great promise for density estimation and data clustering. Given the size of ...
We present in this paper a novel method for eliciting the conditional probability matrices needed for a Bayesian network with the help of a neural network. We demonstrate how we c...
Mathematical models of physical phenomena are of critical importance in virtually all applications of science and technology. This paper addresses the problem of how to use data to...
Anthony M. D'Amato, Aaron J. Ridley, Dennis S. Ber...
Many sensing techniques and image processing applications are characterized by noisy, or corrupted, image data. Anisotropic diffusion is a popular, and theoretically well understo...
Hanno Scharr, Michael J. Black, Horst W. Haussecke...
We propose a framework for the representation of visual knowledge in a robotic agent, with special attention to the understanding of dynamic scenes. According to our approach, und...
Antonio Chella, Marcello Frixione, Salvatore Gagli...