In this paper we propose novel algorithms for total variation (TV) based image restoration and parameter estimation utilizing variational distribution approximations. By following...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...
In this paper, we formulate the shape localization problem in the Bayesian framework. In the learning stage, we propose the Constrained RankBoost approach to model the likelihood ...
We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing t...
This corresponds to the material in the invited keynote presentation by H. J. Siegel, summarizing the research in [2, 23]. Resource allocation decisions in heterogeneous parallel a...
Vladimir Shestak, Howard Jay Siegel, Anthony A. Ma...
—We address the problem of estimating a random vector X from two sets of measurements Y and Z, such that the estimator is linear in Y . We show that the partially linear minimum ...