Bayesian learning, widely used in many applied data-modeling problems, is often accomplished with approximation schemes because it requires intractable computation of the posterio...
Visualization and analysis of the micro-architecture of brain parenchyma by means of magnetic resonance imaging is nowadays believed to be one of the most powerful tools used for ...
It is well-known that wavelet transforms provide sparse decompositions over many types of image regions but not over image singularities/edges that manifest themselves along curve...
Broadcast scheduling is a fundamental problem in wireless ad hoc networks. The objective of a broadcast schedule is to deliver a message from a given source to all other nodes in ...
Reza Mahjourian, Feng Chen, Ravi Tiwari, My T. Tha...
In this paper we focus on an interpretation of Gaussian radial basis functions (GRBF) which motivates extensions and learning strategies. Specifically, we show that GRBF regressio...