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» Learning the Structure of Linear Latent Variable Models
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NIPS
2007
13 years 9 months ago
People Tracking with the Laplacian Eigenmaps Latent Variable Model
Reliably recovering 3D human pose from monocular video requires models that bias the estimates towards typical human poses and motions. We construct priors for people tracking usi...
Zhengdong Lu, Miguel Á. Carreira-Perpi&ntil...
PKDD
2010
Springer
160views Data Mining» more  PKDD 2010»
13 years 5 months ago
Entropy and Margin Maximization for Structured Output Learning
Abstract. We consider the problem of training discriminative structured output predictors, such as conditional random fields (CRFs) and structured support vector machines (SSVMs)....
Patrick Pletscher, Cheng Soon Ong, Joachim M. Buhm...
ICML
2007
IEEE
14 years 8 months ago
Linear and nonlinear generative probabilistic class models for shape contours
We introduce a robust probabilistic approach to modeling shape contours based on a lowdimensional, nonlinear latent variable model. In contrast to existing techniques that use obj...
Graham McNeill, Sethu Vijayakumar
EMNLP
2007
13 years 9 months ago
Fast and Robust Multilingual Dependency Parsing with a Generative Latent Variable Model
We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a rec...
Ivan Titov, James Henderson
EMNLP
2008
13 years 9 months ago
Sparse Multi-Scale Grammars for Discriminative Latent Variable Parsing
We present a discriminative, latent variable approach to syntactic parsing in which rules exist at multiple scales of refinement. The model is formally a latent variable CRF gramm...
Slav Petrov, Dan Klein