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...
We present a framework for implementing geometric algorithms involving motion. It is written in C++ and modeled after and makes extensive use of CGAL (Computational Geometry Algor...
Leonidas J. Guibas, Menelaos I. Karavelas, Daniel ...
The visualisation pipeline approach is a flexible and extensible technique for generating visualisations. The basic pipeline functions involve the capture and representation of da...
We present a method for real-time level of detail reduction that is able to display high-complexity polygonal surface data. A compact and efficient regular grid representation is...
Abstract. This paper introduces a formalization of a set of spatial semantic integrity constraints on an extended-relational database model. The formalization extends traditional n...