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104
Voted
CVPR
1999
IEEE
16 years 4 months ago
A Novel Bayesian Method for Fitting Parametric and Non-Parametric Models to Noisy Data
We o er a simple paradigm for tting models, parametric and non-parametric, to noisy data, which resolves some of the problems associated with classic MSE algorithms. This is done ...
Michael Werman, Daniel Keren
ICAC
2005
IEEE
15 years 8 months ago
Decentralised Autonomic Computing: Analysing Self-Organising Emergent Behaviour using Advanced Numerical Methods
When designing decentralised autonomic computing systems, a fundamental engineering issue is to assess systemwide behaviour. Such decentralised systems are characterised by the la...
Tom De Wolf, Giovanni Samaey, Tom Holvoet, Dirk Ro...
115
Voted
HAPTICS
2005
IEEE
15 years 8 months ago
A Closest Point Algorithm for Parametric Surfaces with Global Uniform Asymptotic Stability
— We present an algorithm that determines the point on a convex parametric surface patch that is closest to a given (possibly moving) point. Any initial point belonging to the su...
Volkan Patoglu, R. Brent Gillespie
114
Voted
CVPR
2010
IEEE
15 years 8 months ago
Pushing the Envelope of Modern Methods for Bundle Adjustment
In this paper, we present results and experiments with several methods for bundle adjustment, producing the fastest bundle adjuster ever published. The fastest methods work with t...
Yekeun Jeong, David Nister, Drew Steedly, Richard ...
ICASSP
2011
IEEE
14 years 6 months ago
Fast adaptive variational sparse Bayesian learning with automatic relevance determination
In this work a new adaptive fast variational sparse Bayesian learning (V-SBL) algorithm is proposed that is a variational counterpart of the fast marginal likelihood maximization ...
Dmitriy Shutin, Thomas Buchgraber, Sanjeev R. Kulk...