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» Intrinsic Geometries in Learning
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JMLR
2010
136views more  JMLR 2010»
13 years 2 months ago
Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes
Variational Bayesian (VB) methods are typically only applied to models in the conjugate-exponential family using the variational Bayesian expectation maximisation (VB EM) algorith...
Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti...
CVPR
2007
IEEE
14 years 9 months ago
Monocular and Stereo Methods for AAM Learning from Video
The active appearance model (AAM) is a powerful method for modeling deformable visual objects. One of the major drawbacks of the AAM is that it requires a training set of pseudo-d...
Jason Saragih, Roland Göcke
SI3D
2010
ACM
14 years 2 months ago
Learning skeletons for shape and pose
In this paper a method for estimating a rigid skeleton, including skinning weights, skeleton connectivity, and joint positions, given a sparse set of example poses is presented. I...
Nils Hasler, Thorsten Thormählen, Bodo Rosenh...
CPAIOR
2009
Springer
14 years 2 months ago
Learning How to Propagate Using Random Probing
Abstract. In constraint programming there are often many choices regarding the propagation method to be used on the constraints of a problem. However, simple constraint solvers usu...
Efstathios Stamatatos, Kostas Stergiou
GECCO
2009
Springer
14 years 7 days ago
The sensitivity of HyperNEAT to different geometric representations of a problem
HyperNEAT, a generative encoding for evolving artificial neural networks (ANNs), has the unique and powerful ability to exploit the geometry of a problem (e.g., symmetries) by enc...
Jeff Clune, Charles Ofria, Robert T. Pennock