We present a generative model for representing and reasoning about the relationships among events in continuous time. We apply the model to the domain of networked and distributed...
In this paper we present a family of models and learning algorithms that can simultaneously align and cluster sets of multidimensional curves measured on a discrete time grid. Our...
Algorithms for learning the conditional probabilities of Bayesian networks with hidden variables typically operate within a high-dimensional search space and yield only locally op...
This paper snggests a method to align Korean-English parallel corpus. '1?he structural dissimilarity between Korean and Indo-European languages requires more flexible measure...
We extend the approach of model checking parameterized networks of processes by means of network invariants to the setting of real-time systems. We introduce timed transition stru...