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CVIU
2004
132views more  CVIU 2004»
15 years 6 months ago
Layered representations for learning and inferring office activity from multiple sensory channels
We present the use of layered probabilistic representations for modeling human activities, and describe how we use the representation to do sensing, learning, and inference at mul...
Nuria Oliver, Ashutosh Garg, Eric Horvitz
JMLR
2010
88views more  JMLR 2010»
15 years 1 months ago
Inference and Learning in Networks of Queues
Probabilistic models of the performance of computer systems are useful both for predicting system performance in new conditions, and for diagnosing past performance problems. The ...
Charles A. Sutton, Michael I. Jordan
JMLR
2010
143views more  JMLR 2010»
15 years 1 months ago
Beware of the DAG!
Directed acyclic graph (DAG) models are popular tools for describing causal relationships and for guiding attempts to learn them from data. In particular, they appear to supply a ...
A. Philip Dawid
ECCC
2011
171views ECommerce» more  ECCC 2011»
14 years 10 months ago
Testing Linear Properties: Some general themes
The last two decades have seen enormous progress in the development of sublinear-time algorithms — i.e., algorithms that examine/reveal properties of “data” in less time tha...
Madhu Sudan
ICML
2008
IEEE
16 years 7 months ago
The dynamic hierarchical Dirichlet process
The dynamic hierarchical Dirichlet process (dHDP) is developed to model the timeevolving statistical properties of sequential data sets. The data collected at any time point are r...
Lu Ren, David B. Dunson, Lawrence Carin