We introduce standardised building blocks designed to be used with variational Bayesian learning. The blocks include Gaussian variables, summation, multiplication, nonlinearity, a...
Tapani Raiko, Harri Valpola, Markus Harva, Juha Ka...
Modeling subspaces of a distribution of interest in high dimensional spaces is a challenging problem in pattern analysis. In this paper, we present a novel framework for pose inva...
Selection of an optimal estimator typically relies on either supervised training samples (pairs of measurements and their associated true values), or a prior probability model for...
The primitives of point-based representations are independent but are rendered using surfels, which approximate the immediate neighborhood of each point linearly. A large number o...
We introduce a novel probabilistic approach for nonparametric nonrigid image registration using generalized elastic nets, a model previously used for topographic maps. The idea of...