In this paper, we develop the continuous time dynamic topic model (cDTM). The cDTM is a dynamic topic model that uses Brownian motion to model the latent topics through a sequenti...
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...
We examine the problem of Transfer in Reinforcement Learning and present a method to utilize knowledge acquired in one Markov Decision Process (MDP) to bootstrap learning in a mor...
Abstract. In this paper we solve the problem of computing exact continuous optimal curves and surfaces for image segmentation and 3D reconstruction, using a maximal flow approach ...
We describe algorithms for computing Nash equilibria in structured game representations, including both graphical games and multi-agent influence diagrams (MAIDs). The algorithms...