Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
This paper introduces a CAD framework for co-simulation of hybrid circuits containing CMOS and SET (Single Electron Transistor) devices. An improved analytical model for SET is al...
Many bioinformatics studies require the analysis of RNA or DNA structures. More specifically, extensive work is done to elaborate efficient algorithms able to predict the 2-D fold...
Model-based diagnosis applied to computer programs has been studied for several years. Although there are still weaknesses in the used models, especially on dealing with dynamic da...
We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density func...