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JMLR
2010
192views more  JMLR 2010»
13 years 3 days 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
NIPS
2001
13 years 6 months ago
Unsupervised Learning of Human Motion Models
This paper presents an unsupervised learning algorithm that can derive the probabilistic dependence structure of parts of an object (a moving human body in our examples) automatic...
Yang Song, Luis Goncalves, Pietro Perona
NIPS
2008
13 years 6 months ago
Partially Observed Maximum Entropy Discrimination Markov Networks
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...
Jun Zhu, Eric P. Xing, Bo Zhang
TASLP
2008
122views more  TASLP 2008»
13 years 3 months ago
Challenging Uncertainty in Query by Humming Systems: A Fingerprinting Approach
Robust data retrieval in the presence of uncertainty is a challenging problem in multimedia information retrieval. In query-by-humming (QBH) systems, uncertainty can arise in query...
Erdem Unal, Elaine Chew, Panayiotis G. Georgiou, S...
IJCV
2006
171views more  IJCV 2006»
13 years 5 months ago
Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation
We develop a method for the estimation of articulated pose, such as that of the human body or the human hand, from a single (monocular) image. Pose estimation is formulated as a s...
Rómer Rosales, Stan Sclaroff