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JMLR
2010
202views more  JMLR 2010»
14 years 11 months ago
Learning the Structure of Deep Sparse Graphical Models
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...
133
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ICASSP
2011
IEEE
14 years 8 months ago
Exploiting an interplay between norms to analyze scalar quantization schemes
Quantization is intrinsic to several data acquisition systems. This process is especially important in distributed settings, where observations must rst be compressed before they ...
Parimal Parag, Jean-François Chamberland
BNCOD
2011
356views Database» more  BNCOD 2011»
14 years 4 months ago
Achieving High Freshness and Optimal Throughput in CPU-Limited Execution of Multi-join Continuous Queries
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 ...
ICASSP
2009
IEEE
15 years 11 months ago
Structured variational methods for distributed inference in wireless ad hoc and sensor networks
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...
Yanbing Zhang, Huaiyu Dai
ECAI
2008
Springer
15 years 6 months ago
Emergence of Rules in Cell Assemblies of fLIF Neurons
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...
Roman V. Belavkin, Christian R. Huyck