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» Structured Learning with Approximate Inference
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CVPR
2004
IEEE
15 years 11 months ago
A Graphical Model Framework for Coupling MRFs and Deformable Models
This paper proposes a new framework for image segmentation based on the integration of MRFs and deformable models using graphical models. We first construct a graphical model to r...
Rui Huang, Vladimir Pavlovic, Dimitris N. Metaxas
ACCV
2007
Springer
15 years 3 months ago
Converting Thermal Infrared Face Images into Normal Gray-Level Images
In this paper, we address the problem of producing visible spectrum facial images as we normally see by using thermal infrared images. We apply Canonical Correlation Analysis (CCA)...
Mingsong Dou, Chao Zhang, Pengwei Hao, Jun Li
AAAI
1992
14 years 10 months ago
Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning
It is often useful for a robot to construct a spatial representation of its environment from experiments and observations, in other words, to learn a map of its environment by exp...
Thomas Dean, Dana Angluin, Kenneth Basye, Sean P. ...
91
Voted
UAI
2008
14 years 11 months ago
Learning Arithmetic Circuits
Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, be...
Daniel Lowd, Pedro Domingos
JMLR
2010
137views more  JMLR 2010»
14 years 4 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton