Most predominant approaches in probabilistic planning utilize techniques from the more thoroughly investigated field of classical planning by determinizing the problem at hand. I...
We discuss algorithms for generation within the Lambek Theorem Proving Framework. Efficient algorithms for generation in this framework take a semantics-driven strategy. This stra...
We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic pro...
Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, T...
Background: Generalized hidden Markov models (GHMMs) appear to be approaching acceptance as a de facto standard for state-of-the-art ab initio gene finding, as evidenced by the re...
Prediction of individual sequences is investigated for cases in which the decision maker observes a delayed version of the sequence, or is forced to issue his/her predictions a nu...