The complexity of quantitative biomedical models, and the rate at which they are published, is increasing to a point where managing the information has become all but impossible w...
We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to...
Onureena Banerjee, Laurent El Ghaoui, Alexandre d'...
We study a novel "coverage by directional sensors" problem with tunable orientations on a set of discrete targets. We propose a Maximum Coverage with Minimum Sensors (MCM...
Deep Belief Networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton et al., along with a greedy layer-wis...
Today, several costs caused by road traffic may either be only roughly approximated, or cannot be clearly assigned to the drivers causing them, or both. They are typically distribu...