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» Approximate reduction of dynamic systems
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104
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AAAI
2006
15 years 2 months ago
Mixtures of Predictive Linear Gaussian Models for Nonlinear, Stochastic Dynamical Systems
The Predictive Linear Gaussian model (or PLG) improves upon traditional linear dynamical system models by using a predictive representation of state, which makes consistent parame...
David Wingate, Satinder P. Singh
103
Voted
ICML
2006
IEEE
16 years 1 months ago
Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems
The recent Predictive Linear Gaussian model (or PLG) improves upon traditional linear dynamical system models by using a predictive representation of state, which makes consistent...
David Wingate, Satinder P. Singh
HPDC
2008
IEEE
15 years 29 days ago
Code coverage, performance approximation and automatic recognition of idioms in scientific applications
Basic data flow patterns which we call idioms, such as stream, transpose, reduction, random access and stencil, are common in scientific numerical applications. We hypothesize tha...
Jiahua He, Allan Snavely, Rob F. Van der Wijngaart...
CISS
2011
IEEE
14 years 4 months ago
Sparsity penalties in dynamical system estimation
—In this work we address the problem of state estimation in dynamical systems using recent developments in compressive sensing and sparse approximation. We formulate the traditio...
Adam Charles, Muhammad Salman Asif, Justin K. Romb...
103
Voted
ICCV
2005
IEEE
16 years 2 months ago
Dynamic Measurement Clustering to Aid Real Time Tracking
Many parameter estimation problems admit divide and conquer or partitioning techniques in order to reduce a highdimensional task into several reduced-dimension problems. These tec...
Christopher Kemp, Tom Drummond