Abstract. We propose and analyze a new vantage point for the learning of mixtures of Gaussians: namely, the PAC-style model of learning probability distributions introduced by Kear...
We show how to uniformly distribute data at random (not to be confounded with permutation routing) in two settings that are able to deal with massive data: coarse grained parallel...
Abstract. Sreedhar et al. [SGL98, Sre95] have presented an eliminationbased algorithm to solve data flow problems. A thorough analysis of the algorithm shows that the worst-case pe...
In order to perform object recognition it is necessary to learn representations of the underlying components of images. Such components correspond to objects, object-parts, or fea...
The paper extends the idea of negative representations of information for enhancing privacy. Simply put, a set DB of data elements can be represented in terms of its complement set...
Fernando Esponda, Elena S. Ackley, Paul Helman, Ha...