Graphical models are powerful tools for processing images. However, the large dimensionality of even local image data poses a difficulty: representing the range of possible graphi...
Marshall F. Tappen, Bryan C. Russell, William T. F...
We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...
—We consider distributed estimation of the inverse covariance matrix in Gaussian graphical models. These models factorize the multivariate distribution and allow for efficient d...
This paper presents a plan-based model that handles negotiation subdialogues by inferring both the communicative actions that people pursue when speaking and the beliefs underlyin...
This paper describes a method for risk analysis based on the approach used in CRAMM, but instead of using discrete measures for threats and vulnerabilities and lookup tables to de...