Dynamic Bayesian networks (DBNs) offer an elegant way to integrate various aspects of language in one model. Many existing algorithms developed for learning and inference in DBNs ...
Classical learning assumes the learner is given a labeled data sample, from which it learns a model. The field of Active Learning deals with the situation where the learner begins...
Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature of the system under consideration. A close match between the input model and the...
Evaluating competing technologies on a common problem set is a powerful way to improve the state of the art and hasten technology transfer. Yet poorly designed evaluations can was...
We present an algorithm for meshing surfaces that is a simple adaptation of a greedy “farthest point” technique proposed by Chew. Given a surface S, it progressively adds poin...