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» Modeling Classification and Inference Learning
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ICML
2010
IEEE
13 years 10 months ago
Learning Efficiently with Approximate Inference via Dual Losses
Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Glo...
AAAI
2008
13 years 11 months ago
Latent Tree Models and Approximate Inference in Bayesian Networks
We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and wh...
Yi Wang, Nevin Lianwen Zhang, Tao Chen
CIKM
2009
Springer
14 years 3 months ago
Learning to rank from Bayesian decision inference
Ranking is a key problem in many information retrieval (IR) applications, such as document retrieval and collaborative filtering. In this paper, we address the issue of learning ...
Jen-Wei Kuo, Pu-Jen Cheng, Hsin-Min Wang
ICML
2009
IEEE
14 years 10 months ago
Polyhedral outer approximations with application to natural language parsing
Recent approaches to learning structured predictors often require approximate inference for tractability; yet its effects on the learned model are unclear. Meanwhile, most learnin...
André F. T. Martins, Noah A. Smith, Eric P....
CVPR
2008
IEEE
14 years 3 months ago
3D face tracking and expression inference from a 2D sequence using manifold learning
We propose a person-dependent, manifold-based approach for modeling and tracking rigid and nonrigid 3D facial deformations from a monocular video sequence. The rigid and nonrigid ...
Wei-Kai Liao, Gérard G. Medioni