We combine the strengths of Bayesian modeling and synchronous grammar in unsupervised learning of basic translation phrase pairs. The structured space of a synchronous grammar is ...
Hao Zhang, Chris Quirk, Robert C. Moore, Daniel Gi...
Many nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switc...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...
We propose a new method for comparing learning algorithms on multiple tasks which is based on a novel non-parametric test that we call the Poisson binomial test. The key aspect of...
We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration vers...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...
Abstract. This paper presents a new Bayesian approach to hyperspectral image segmentation that boosts the performance of the discriminative classifiers. This is achieved by combin...