We present efficient algorithms for dealing with the problem of missing inputs (incomplete feature vectors) during training and recall. Our approach is based on the approximation ...
We study a time series model that can be viewed as a decision tree with Markov temporal structure. The model is intractable for exact calculations, thus we utilize variational app...
Michael I. Jordan, Zoubin Ghahramani, Lawrence K. ...
In this work, we present a general method for approximating nonlinear transformations of Gaussian mixture random variables. It is based on transforming the individual Gaussians wi...
The problem of task assignment in heterogeneous computing systems has been studied for many years with many variations. We have developed a new hybrid approximation algorithm. The...
We present a method to efficiently construct and render a smooth surface for approximation of large functional scattered data. Using a subdivision surface framework and techniques...