We present a novel probabilistic multiple cause model for binary observations. In contrast to other approaches, the model is linear and it infers reasons behind both observed and ...
We propose a corpus-based probabilistic framework to extract hidden common syntax across languages from non-parallel multilingual corpora in an unsupervised fashion. For this purp...
In this paper, we propose a novel technique on mining relationships in a sequential circuit to discover global constraints. In contrast to the traditional learning methods, our mi...
Constraint Satisfaction Problems are ubiquitous in Artificial Intelligence. Over the past decade significant advances have been made in terms of the size of problem instance tha...
Margarita Razgon, Barry O'Sullivan, Gregory M. Pro...
We present a hybrid approach to Distributed Constraint Satisfaction which combines incomplete, fast, penalty-based local search with complete, slower systematic search. Thus, we pr...