Gerda Janssens

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Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks such as computing the marginals given evidence and learning from (partial)(More)
Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or data mining. However, in order for ILP to become practically useful, the eÆciency of ILP systems must improve substantially. To this end, the notion of a query pack is introduced: it structures sets of similar queries. Furthermore, a mechanism is described(More)
Programs for embedded multimedia applications typically manipulate several large multi-dimensional arrays. The energy consumption per access increases with their size; the access to these large arrays is responsible for a substantial part of the power consumption. In this paper, an analysis is developed to compute a bounding box for the elements in the(More)
Designers often apply manual or semi-automatic loop and data transformations on array- and loop-intensive programs to improve performance. It is crucial that such transformations preserve the functionality of the program. This article presents an automatic method for constructing equivalence proofs for the class of static affine programs. The equivalence(More)
Relatively simple transformations can speed up the execution of queries for data mining considerably. While some ILP systems use such transformations, relatively little is known about them or how they relate to each other. This paper describes a number of such transformations. Not all of them are novel, but there have been no studies comparing their(More)
This paper describes a novel application of abstract interpretation dealing with mixed systems of numerical and unification constraints. In general, the abstraction of such systems cannot be separated into two independent parts, since a program variable is often involved in both types of constraints. The proposed abstraction deals in an elegant way with the(More)
Thesis — Automatic and efficient verification of loop and data-flow transformations commonly applied while optimizing digital signal processing and scientific computing programs is feasible by functional equivalence checking of the original and transformed programs. Application of transformations, in general, is known to enable efficient implementation of(More)
Constraint Programming (CP) is a high-level declarative programming paradigm in which problems are modeled by means of constraints on the problem variables that need to hold in all solutions to the problem. Many problems of high practical relevance can easily be described in terms of constraints. Example application areas include production planning and(More)