We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. As an extension of Welling and ...
Minimum mean squared error estimates generally are not optimal in terms of a common track error statistic used in tracking benchmarks, namely a form of the Mean Optimal Subpattern...
David Frederic Crouse, Peter Willett, Yaakov Bar-S...
We design a family of non-local image smoothing algorithms which approximate the application of diffusion PDE's on a specific Euclidean space of image patches. We first map a...
Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While ...
Two-photon calcium imaging is an emerging experimental technique that enables the study of information processing within neural circuits in vivo. While the spatial resolution of th...
Eva L. Dyer, Marco F. Duarte, Don H. Johnson, Rich...