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» Imitation Learning Using Graphical Models
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ICML
2004
IEEE
14 years 10 months ago
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
John D. Lafferty, Xiaojin Zhu, Yan Liu
CORR
2011
Springer
230views Education» more  CORR 2011»
13 years 4 months ago
Computational Rationalization: The Inverse Equilibrium Problem
Modeling the behavior of imperfect agents from a small number of observations is a difficult, but important task. In the singleagent decision-theoretic setting, inverse optimal co...
Kevin Waugh, Brian Ziebart, J. Andrew Bagnell
EUROGRAPHICS
2010
Eurographics
14 years 6 months ago
Synthesis of Responsive Motion Using a Dynamic Model
Synthesizing the movements of a responsive virtual character in the event of unexpected perturbations has proven a difficult challenge. To solve this problem, we devise a fully a...
Yuting Ye and C. Karen Liu
AMDO
2006
Springer
14 years 1 months ago
Monocular Tracking with a Mixture of View-Dependent Learned Models
This paper considers the problem of monocular human body tracking using learned models. We propose to learn the joint probability distribution of appearance and body pose using a m...
Tobias Jaeggli, Esther Koller-Meier, Luc J. Van Go...
CORR
2011
Springer
174views Education» more  CORR 2011»
13 years 1 months ago
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a ...
Yoshitaka Kameya, Taisuke Sato