We present an original approach for motion-based retrieval involving partial query. More precisely, we propose an uni ed statistical framework both to extract entities of interest ...
This paper presents an innovative model of a program’s internal behavior over a set of test inputs, called the probabilistic program dependence graph (PPDG), that facilitates pr...
This paper introduces LDA-G, a scalable Bayesian approach to finding latent group structures in large real-world graph data. Existing Bayesian approaches for group discovery (suc...
Data structures define how values being computed are stored and accessed within programs. By recognizing what data structures are being used in an application, tools can make app...
We present a novel approach to weakly supervised semantic class learning from the web, using a single powerful hyponym pattern combined with graph structures, which capture two pr...