— We consider the problem of finding sufficiently simple models of high-dimensional physical systems that are consistent with observed trajectories, and using these models to s...
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
In recent years, mobile ad-hoc networks (MANET’s) have been deployed in various scenarios, but their scalability is severely restricted by the human operators’ ability to conf...
Abstract. In recent years, mobile ad-hoc networks (MANET’s) have been deployed in various scenarios, but their scalability is severely restricted by the human operators’ abilit...
This paper reports on our recent work on modeling and automatically extracting vague, implicit event durations from text (Pan et al., 2006a, 2006b). It is a kind of commonsense kn...