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» The Inference Problem: A Survey
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JMLR
2010
145views more  JMLR 2010»
15 years 2 months ago
Parallelizable Sampling of Markov Random Fields
Markov Random Fields (MRFs) are an important class of probabilistic models which are used for density estimation, classification, denoising, and for constructing Deep Belief Netwo...
James Martens, Ilya Sutskever
TSP
2010
15 years 2 months ago
Gaussian multiresolution models: exploiting sparse Markov and covariance structure
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
FGR
2011
IEEE
272views Biometrics» more  FGR 2011»
14 years 11 months ago
Adaptive discriminant analysis for face recognition from single sample per person
—Discriminant analysis, especially Fisherface and its numerous variants, have achieved great success in face recognition. However, these methods fail to work for face recognition...
Meina Kan, Shiguang Shan, Yu Su, Xilin Chen, Wen G...
CCS
2011
ACM
14 years 7 months ago
Automatically optimizing secure computation
On the one hand, compilers for secure computation protocols, such as FairPlay or FairPlayMP, have significantly simplified the development of such protocols. On the other hand, ...
Florian Kerschbaum
CORR
2012
Springer
183views Education» more  CORR 2012»
14 years 3 months ago
Learning Determinantal Point Processes
Determinantal point processes (DPPs), which arise in random matrix theory and quantum physics, are natural models for subset selection problems where diversity is preferred. Among...
Alex Kulesza, Ben Taskar