Run-time parallelization is often the only way to execute the code in parallel when data dependence information is incomplete at compile time. This situation is common in many imp...
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...
Background: Metabolic networks present a complex interconnected structure, whose understanding is in general a non-trivial task. Several formal approaches have been developed to s...
In the past decade or so, subspace methods have been largely used in face recognition ? generally with quite success. Subspace approaches, however, generally assume the training d...
There is an increasing quantity of data with uncertainty arising from applications such as sensor network measurements, record linkage, and as output of mining algorithms. This un...