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ICML
2007
IEEE
14 years 8 months ago
Hierarchical Gaussian process latent variable models
The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we ext...
Neil D. Lawrence, Andrew J. Moore
CORR
2012
Springer
187views Education» more  CORR 2012»
12 years 3 months ago
Sequential Inference for Latent Force Models
Latent force models (LFMs) are hybrid models combining mechanistic principles with non-parametric components. In this article, we shall show how LFMs can be equivalently formulate...
Jouni Hartikainen, Simo Särkkä
NIPS
2007
13 years 9 months ago
People Tracking with the Laplacian Eigenmaps Latent Variable Model
Reliably recovering 3D human pose from monocular video requires models that bias the estimates towards typical human poses and motions. We construct priors for people tracking usi...
Zhengdong Lu, Miguel Á. Carreira-Perpi&ntil...
ICCV
2011
IEEE
12 years 7 months ago
Learning to Cluster Using High Order Graphical Models with Latent Variables
This paper proposes a very general max-margin learning framework for distance-based clustering. To this end, it formulates clustering as a high order energy minimization problem w...
Nikos Komodakis
ISLPED
1996
ACM
81views Hardware» more  ISLPED 1996»
13 years 11 months ago
Simulation based architectural power estimation for PLA-based controllers
We present an architectural power simulation technique for PLA-based controllers. The contributions of this work are (1) a simple but ecient power characterization of PLAs; and (2...
Srinivas Katkoori, Ranga Vemuri