Abstract—We present a unified graphical model framework for describing compound codes and deriving iterative decoding algorithms. After reviewing a variety of graphical models (...
Abstract. The Transferable Belief Model (TBM) relies on belief functions and enables one to represent and combine a variety of knowledge from certain up to ignorance as well as con...
Abstract— We propose a planning algorithm that allows usersupplied domain knowledge to be exploited in the synthesis of information feedback policies for systems modeled as parti...
Salvatore Candido, James C. Davidson, Seth Hutchin...
We propose a hybrid generative/discriminative framework for semantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HMSV...
Abstract. In this paper, we consider the problem of filtering in relational hidden Markov models. We present a compact representation for such models and an associated logical par...
Luke S. Zettlemoyer, Hanna M. Pasula, Leslie Pack ...