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» Modelling Smooth Paths Using Gaussian Processes
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HUMO
2007
Springer
14 years 2 months ago
Modeling Human Locomotion with Topologically Constrained Latent Variable Models
Abstract. Learned, activity-specific motion models are useful for human pose and motion estimation. Nevertheless, while the use of activityspecific models simplifies monocular t...
Raquel Urtasun, David J. Fleet, Neil D. Lawrence
GECCO
2006
Springer
174views Optimization» more  GECCO 2006»
14 years 6 days ago
Optimizing of NC tool paths for five-axis milling using evolutionary algorithms on wavelets
Computer aided NC-path generation of five-axis milling using a standard CAM-system does usually not take machine dynamics and kinematics into account. This results in machine move...
Klaus Weinert, Andreas Zabel, Heinrich Müller...
NIPS
2000
13 years 10 months ago
Mixtures of Gaussian Processes
We introduce the mixture of Gaussian processes (MGP) model which is useful for applications in which the optimal bandwidth of a map is input dependent. The MGP is derived from the...
Volker Tresp
MICCAI
2008
Springer
14 years 9 months ago
MR Brain Tissue Classification Using an Edge-Preserving Spatially Variant Bayesian Mixture Model
In this paper, a spatially constrained mixture model for the segmentation of MR brain images is presented. The novelty of this work is a new, edge preserving, smoothness prior whic...
Giorgos Sfikas, Christophoros Nikou, Nikolas P. ...
NIPS
2007
13 years 10 months ago
Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes
Neural spike trains present challenges to analytical efforts due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smooth...
John P. Cunningham, Byron M. Yu, Krishna V. Shenoy...