A Bayesian belief network is a model of a joint distribution over a finite set of variables, with a DAG structure representing immediate dependencies among the variables. For each...
Bayesian parameter estimation can be used to generate statistically optimal solutions to the problem of cue integration. However, the complexity and dimensionality of these solutio...
We show that categories induced by unsupervised word clustering can surpass the performance of gold part-of-speech tags in dependency grammar induction. Unlike classic clustering ...
Valentin I. Spitkovsky, Hiyan Alshawi, Angel X. Ch...
Most pose robust face verification algorithms, which employ 2D appearance, rely heavily on statistics gathered from offline databases containing ample facial appearance variation ...
The availability of whole genome sequences and high-throughput genomic assays opens the door for in silico analysis of transcription regulation. This includes methods for discover...
Yoseph Barash, Gal Elidan, Nir Friedman, Tommy Kap...