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163
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CVPR
2011
IEEE
15 years 3 hour ago
On Deep Generative Models with Applications to Recognition
The most popular way to use probabilistic models in vision is first to extract some descriptors of small image patches or object parts using well-engineered features, and then to...
Marc', Aurelio Ranzato, Joshua Susskind, Volodymyr...
124
Voted
INTERSPEECH
2010
14 years 10 months ago
Dynamic model selection for spectral voice conversion
Statistical methods for voice conversion are usually based on a single model selected in order to represent a tradeoff between goodness of fit and complexity. In this paper we ass...
Pierre Lanchantin, Xavier Rodet
139
Voted
ICASSP
2011
IEEE
14 years 7 months ago
Sparse graphical modeling of piecewise-stationary time series
Graphical models are useful for capturing interdependencies of statistical variables in various fields. Estimating parameters describing sparse graphical models of stationary mul...
Daniele Angelosante, Georgios B. Giannakis
154
Voted
ICCV
2011
IEEE
14 years 3 months ago
Optimal Landmark Detection using Shape Models and Branch and Bound
Fitting statistical 2D and 3D shape models to images is necessary for a variety of tasks, such as video editing and face recognition. Much progress has been made on local fitting...
Brian Amberg, Thomas Vetter
155
Voted
JMLR
2012
13 years 6 months ago
Low rank continuous-space graphical models
Constructing tractable dependent probability distributions over structured continuous random vectors is a central problem in statistics and machine learning. It has proven diffic...
Carl Smith, Frank Wood, Liam Paninski