In this paper we investigate a mesh-modeling approach for multi-modality image reconstruction. In the proposed approach a mesh model uses information obtained from an anatomical M...
Jovan G. Brankov, Yongyi Yang, Richard M. Leahy, M...
We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and wh...
Computer simulations can be used to teach complicated statistical concepts in linear regression more quickly and effectively than traditional lecture alone. In introductory applie...
Recent advances in statistical machine translation have used approximate beam search for NP-complete inference within probabilistic translation models. We present an alternative ap...
Abhishek Arun, Barry Haddow, Philipp Koehn, Adam L...
Abstract--We investigate parameter-based and distributionbased approaches to regularizing the generative, similarity-based classifier called local similarity discriminant analysis ...