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JMLR
2012
13 years 4 days ago
Gaussian Processes for time-marked time-series data
In many settings, data is collected as multiple time series, where each recorded time series is an observation of some underlying dynamical process of interest. These observations...
John Cunningham, Zoubin Ghahramani, Carl Edward Ra...
BIOINFORMATICS
2010
108views more  BIOINFORMATICS 2010»
14 years 8 months ago
Mass spectrometry data processing using zero-crossing lines in multi-scale of Gaussian derivative wavelet
Motivation: Peaks are the key information in Mass Spectrometry (MS) which has been increasingly used to discover diseases related proteomic patterns. Peak detection is an essentia...
Nha Nguyen, Heng Huang, Soontorn Oraintara, An P. ...
ICASSP
2011
IEEE
14 years 1 months ago
Nonstationary and temporally correlated source separation using Gaussian process
Blind source separation (BSS) is a process to reconstruct source signals from the mixed signals. The standard BSS methods assume a fixed set of stationary source signals with the ...
Hsin-Lung Hsieh, Jen-Tzung Chien
CVPR
2006
IEEE
15 years 11 months ago
3D People Tracking with Gaussian Process Dynamical Models
We advocate the use of Gaussian Process Dynamical Models (GPDMs) for learning human pose and motion priors for 3D people tracking. A GPDM provides a lowdimensional embedding of hu...
Raquel Urtasun, David J. Fleet, Pascal Fua
ICML
2007
IEEE
15 years 10 months ago
Most likely heteroscedastic Gaussian process regression
This paper presents a novel Gaussian process (GP) approach to regression with inputdependent noise rates. We follow Goldberg et al.'s approach and model the noise variance us...
Kristian Kersting, Christian Plagemann, Patrick Pf...