Abstract--It is critical to use automated generators for synthetic models and data, given the sparsity of benchmark models for empirical analysis and the cost of generating models ...
We present an approach for learning models that obtain accurate classification of large scale data objects, collected in spatiotemporal domains. The model generation is structured ...
Igor Vainer, Sarit Kraus, Gal A. Kaminka, Hamutal ...
This paper introduces a uniform statistical framework for both 3-D and 2-D object recognition using intensity images as input data. The theoretical part provides a mathematical too...
The stochastic discrimination (SD) theory considers learning as building models of uniform coverage over data distributions. Despite successful trials of the derived SD method in s...
Abstract. Model generation is an important formal technique for finding interesting instances of computationally hard problems. In this paper we study model generation over Horn lo...
With computer systems becoming ever larger and more complex, the cost and effort associated with their construction is increasing and the systems are now sufficiently complex that...
Testing model transformations requires input models which are graphs of inter-connected objects that must conform to a meta-model and meta-constraints from heterogeneous sources su...
Abstract. We study the problem of generating a database and parameters for a given parameterized SQL query satisfying a given test condition. We introduce a formal background theor...
Margus Veanes, Pavel Grigorenko, Peli de Halleux, ...