A “graphics for vision” approach is proposed to address the problem of reconstruction from a large and imperfect data set: reconstruction on demand by tensor voting, or ROD-TV...
In this paper we present an innovative approach to automatically generate adjacency grammars describing graphical symbols. A grammar production is formulated in terms of rulesets ...
We present our work on using statistical, corpus-based machine learning techniques to simultaneously recognize an agent's current goal schemas at various levels of a hierarch...
We use graphical models to explore the question of how people learn simple causal relationships from data. The two leading psychological theories can both be seen as estimating th...
We develop a probabilistic modeling framework for multiway arrays. Our framework exploits the link between graphical models and tensor factorization models and it can realize any ...