With the increasing popularity of largescale probabilistic graphical models, even "lightweight" approximate inference methods are becoming infeasible. Fortunately, often...
In non-ergodic belief networks the posterior belief of many queries given evidence may become zero. The paper shows that when belief propagation is applied iteratively over arbitr...
Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-N...
Dror Baron, Shriram Sarvotham, Richard G. Baraniuk
Probabilistic inference in graphical models is a prevalent task in statistics and artificial intelligence. The ability to perform this inference task efficiently is critical in l...
The FastInf C++ library is designed to perform memory and time efficient approximate inference in large-scale discrete undirected graphical models. The focus of the library is pro...
Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elida...