Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
Quantization is intrinsic to several data acquisition systems. This process is especially important in distributed settings, where observations must rst be compressed before they ...
Due to high data volumes and unpredictable arrival rates, continuous query systems processing expensive queries in real-time may fail to keep up with the input data streams - resul...
Abhishek Mukherji, Elke A. Rundensteiner, Matthew ...
Abstract –In this paper, a variational message passing framework is proposed for Markov random fields, which is computationally more efficient and admits wider applicability comp...
Abstract. There are many examples of intelligent and learning systems that are based either on the connectionist or the symbolic approach. Although the latter can be successfully c...