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» Learning the Structure of Deep Sparse Graphical Models
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JMLR
2010
169views more  JMLR 2010»
13 years 3 months ago
Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images
Deep belief nets have been successful in modeling handwritten characters, but it has proved more difficult to apply them to real images. The problem lies in the restricted Boltzma...
Marc'Aurelio Ranzato, Alex Krizhevsky, Geoffrey E....
ICASSP
2010
IEEE
13 years 9 months ago
Learning sparse systems at sub-Nyquist rates: A frequency-domain approach
We propose a novel algorithm for sparse system identification in the frequency domain. Key to our result is the observation that the Fourier transform of the sparse impulse respo...
Martin McCormick, Yue M. Lu, Martin Vetterli
ICCV
2009
IEEE
15 years 1 months ago
Modelling Activity Global Temporal Dependencies using Time Delayed Probabilistic Graphical Model
We present a novel approach for detecting global behaviour anomalies in multiple disjoint cameras by learning time delayed dependencies between activities cross camera views. Sp...
Chen Change Loy, Tao Xiang and Shaogang Gong
ICDAR
2007
IEEE
14 years 3 months ago
Energy-Based Models in Document Recognition and Computer Vision
The Machine Learning and Pattern Recognition communities are facing two challenges: solving the normalization problem, and solving the deep learning problem. The normalization pro...
Yann LeCun, Sumit Chopra, Marc'Aurelio Ranzato, Fu...
CLOR
2006
14 years 12 days ago
A Sparse Object Category Model for Efficient Learning and Complete Recognition
We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a weakly-supervised manner: the model is learnt from examp...
Robert Fergus, Pietro Perona, Andrew Zisserman