Dependency networks are a compelling alternative to Bayesian networks for learning joint probability distributions from data and using them to compute probabilities. A dependency ...
Many researchers have used lexical networks and ontologies to mitigate synonymy and polysemy problems in Question Answering (QA), systems coupled with taggers, query classifiers,...
: Our research has shown that schedules can be built mimicking a human scheduler by using a set of rules that involve domain knowledge. This chapter presents a Bayesian Optimizatio...
— In this paper, we present an approach allowing a robot to learn a generative model of its own physical body from scratch using self-perception with a single monocular camera. O...
We present node level primitives for parallel exact inference on an arbitrary Bayesian network. We explore the probability representation on each node of Bayesian networks and eac...