Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...
The study presented in this paper analyses descriptions extracted with MPEG-7-descriptors from visual content from the statistical point of view. Good descriptors should generate ...
Internet servers need to be highly-available, inexpensive, and scalable. These goals are often con icting and most designs meet, with limited success, only few of them. In this pa...
We describe a neurally-inspired, unsupervised learning algorithm that builds a non-linear generative model for pairs of face images from the same individual. Individuals are then ...
In this paper, we concentrate on the expressive power of hierarchical structures in neural networks. Recently, the so-called SplitNet model was introduced. It develops a dynamic n...