Causal Probabilistic Networks (CPNs), (a.k.a. Bayesian Networks, or Belief Networks) are well-established representations in biomedical applications such as decision support system...
Constantin F. Aliferis, Ioannis Tsamardinos, Alexa...
To date, the principal use case for schema matching research has been as a precursor for code generation, i.e., constructing mappings between schema elements with the end goal of ...
Ken Smith, Michael Morse, Peter Mork, Maya Hao Li,...
Model-driven development (MDD) is an emerging paradigm that improves the software development lifecycle, particularly for large software systems by providing a higherabstraction fo...
Krishnakumar Balasubramanian, Aniruddha S. Gokhale...
Hidden Markov models (HMMs) are effective tools to detect series of statistically homogeneous structures, but they are not well suited to analyse complex structures. Numerous meth...
Christelle Melo de Lima, Laurent Gueguen, Christia...
This paper explores various aspects of the image decomposition problem using modern variational techniques. We aim at splitting an original image f into two components u and v, whe...