Variational methods for approximate inference in machine learning often adapt a parametric probability distribution to optimize a given objective function. This view is especially ...
Antti Honkela, Matti Tornio, Tapani Raiko, Juha Ka...
: Suppose we need to watch a set of targets continuously for a required period of time, and suppose we choose any number of sensors from a fixed set of sensor types and place them ...
We address the problem of computing approximate answers to continuous sliding-window joins over data streams when the available memory may be insufficient to keep the entire join...
Abstract. We consider the train delivery problem which is a generalization of the bin packing problem and is equivalent to a one dimensional version of the vehicle routing problem ...
This paper presents a novel spatial texture prediction method based on non-negative matrix factorization. As an extension of template matching, approximation based iterative textu...