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» Discriminated Belief Propagation
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CVPR
2009
IEEE
1390views Computer Vision» more  CVPR 2009»
15 years 3 months ago
Stacks of Convolutional Restricted Boltzmann Machines for Shift-Invariant Feature Learning
In this paper we present a method for learning classspecific features for recognition. Recently a greedy layerwise procedure was proposed to initialize weights of deep belief ne...
Mohammad Norouzi (Simon Fraser University), Mani R...
ICASSP
2009
IEEE
14 years 3 months ago
Comparing maximum a posteriori vector quantization and Gaussian mixture models in speaker verification
Gaussian mixture model - universal background model (GMMUBM) is a standard reference classifier in speaker verification. We have recently proposed a simplified model using vect...
Tomi Kinnunen, Juhani Saastamoinen, Ville Hautam&a...
EUROITV
2008
Springer
13 years 10 months ago
What You Expect Is What You See
In this paper an experiment was conducted to measure the effect of framing a high definition television (HDTV) clip. One group of participants was told they were watching a brand n...
Dirkjan Joor, Wilco Beekhuizen, Lidwien van de Wij...
JMLR
2010
192views more  JMLR 2010»
13 years 3 months ago
Efficient Learning of Deep Boltzmann Machines
We present a new approximate inference algorithm for Deep Boltzmann Machines (DBM's), a generative model with many layers of hidden variables. The algorithm learns a separate...
Ruslan Salakhutdinov, Hugo Larochelle
CVPR
2009
IEEE
15 years 3 months ago
Alphabet SOUP: A Framework for Approximate Energy Minimization
Many problems in computer vision can be modeled using conditional Markov random fields (CRF). Since finding the maximum a posteriori (MAP) solution in such models is NP-hard, mu...
Stephen Gould (Stanford University), Fernando Amat...