In the constraint programming framework, state-of-the-art static and dynamic decomposition techniques are hard to apply to problems with complete initial constraint graphs. For suc...
This paper addresses an innovative approach to informed enhancement of damaged sound. It uses sparse approximations with a learned dictionary of atoms modeling the main components...
Manuel Moussallam, Pierre Leveau, Si-Mohamed Aziz ...
The bias-variance decomposition is a very useful and widely-used tool for understanding machine-learning algorithms. It was originally developed for squared loss. In recent years,...
In [Jegou, 1993], a decomposition method has been introduced for improving search efficiency in the area of Constraint Satisfaction Problems. This method is based on properties of...
A decomposition of a polygon P is a set of polygons whose geometric union is exactly P. We consider the problem of decomposing a polygon, which may contain holes, using subpolygon...
Caching, symmetries, and search with decomposition are powerful techniques for pruning the search space of constraint problems. In this paper we present an innovative way of effi...
Abstract— A disjoint support decomposition (DSD) is a representation of a Boolean function F obtained by composing two or more simpler component functions such that the component...
Abstract. We consider workflow graphs as a model for the control flow of a business process model and study the problem of workflow graph parsing, i.e., finding the structure of a ...
—In this paper, we present an algorithm for finding a good Ashenhurst decomposition of a switching function. Most current methods for performing this type of decomposition are ba...
Several algorithms for computing the Minkowski sum of two polygons in the plane begin by decomposing each polygon into convex subpolygons. We examine different methods for decompo...