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JCST
2010
139views more  JCST 2010»
13 years 6 months ago
Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution
In the Bayesian mixture modeling framework it is possible to infer the necessary number of components to model the data and therefore it is unnecessary to explicitly restrict the n...
Dilan Görür, Carl Edward Rasmussen
COMSNETS
2012
168views more  COMSNETS 2012»
12 years 3 months ago
Limitations of scanned human copresence encounters for modelling proximity-borne malware
—Patterns of human encounters, which are difficult to observe directly, are fundamental to the propagation of mobile malware aimed at infecting devices in spatial proximity. We ...
James Mitchell, Eamonn O'Neill, Gjergji Zyba, Geof...
JMLR
2012
11 years 10 months ago
Learning Low-order Models for Enforcing High-order Statistics
Models such as pairwise conditional random fields (CRFs) are extremely popular in computer vision and various other machine learning disciplines. However, they have limited expre...
Patrick Pletscher, Pushmeet Kohli
ICML
2007
IEEE
14 years 8 months ago
Incremental Bayesian networks for structure prediction
We propose a class of graphical models appropriate for structure prediction problems where the model structure is a function of the output structure. Incremental Sigmoid Belief Ne...
Ivan Titov, James Henderson
ICML
2007
IEEE
14 years 8 months ago
Restricted Boltzmann machines for collaborative filtering
Most of the existing approaches to collaborative filtering cannot handle very large data sets. In this paper we show how a class of two-layer undirected graphical models, called R...
Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hin...