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COLING
2008
13 years 8 months ago
Exact Inference for Multi-label Classification using Sparse Graphical Models
This paper describes a parameter estimation method for multi-label classification that does not rely on approximate inference. It is known that multi-label classification involvin...
Yusuke Miyao, Jun-ichi Tsujii
ECCV
2008
Springer
14 years 9 months ago
Learning for Optical Flow Using Stochastic Optimization
Abstract. We present a technique for learning the parameters of a continuousstate Markov random field (MRF) model of optical flow, by minimizing the training loss for a set of grou...
Yunpeng Li, Daniel P. Huttenlocher
AAAI
2011
12 years 7 months ago
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
Chloe Kiddon, Pedro Domingos
ECCV
2008
Springer
14 years 9 months ago
Efficiently Learning Random Fields for Stereo Vision with Sparse Message Passing
As richer models for stereo vision are constructed, there is a growing interest in learning model parameters. To estimate parameters in Markov Random Field (MRF) based stereo formu...
Jerod J. Weinman, Lam Tran, Christopher J. Pal
CORR
2006
Springer
99views Education» more  CORR 2006»
13 years 7 months ago
Rational stochastic languages
In probabilistic grammatical inference, a usual goal is to infer a good approximation of an unknown distribution P called a stochastic language. The estimate of P stands in some cl...
François Denis, Yann Esposito