Given semantic descriptions of object classes, zero-shot learning aims to accurately
recognize objects of the unseen classes, from which no examples are available at the …

We introduce a new approach for focusing constraint reasoning using so-called streamlining
constraints. Such constraints partition the solution space to drive the search first towards a …

Stable matching problems have several practical applications. If preference lists are
truncated and contain ties, finding a stable matching with maximal size is computationally …

We present the parallel implementation of a constraint-based Local Search algorithm and
investigate its performance on several hardware platforms with several hundreds or …

In this paper we study the performance of constraint-based local search solvers on a GPU.
The massively parallel architecture of the GPU makes it possible to explore parallelism at …

Classifier combination methods have proved to be an effective tool for increasing the
performance in pattern recognition applications. The rationale of this approach follows from …

We propose a probabilistic model for the parallel execution of Las Vegas algorithms, ie
randomized algorithms whose runtime might vary from one execution to another, even with …

We present a parallel implementation of a constraint-based local search algorithm and
investigate its performance results on hardware with several hundreds of processors. We …

In this paper we address the problem of parallelizing local search. We propose a general
framework where different local search engines cooperate (through communication) in the …

This paper presents a detailed analysis of the scalability and parallelization of Local Search
algorithms for constraint-based and SAT (Boolean satisfiability) solvers. We propose a …