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CDC
2009
IEEE
185views Control Systems» more  CDC 2009»
14 years 8 days ago
Discrete Empirical Interpolation for nonlinear model reduction
A dimension reduction method called Discrete Empirical Interpolation (DEIM) is proposed and shown to dramatically reduce the computational complexity of the popular Proper Orthogo...
Saifon Chaturantabut, Danny C. Sorensen
JMLR
2006
143views more  JMLR 2006»
13 years 7 months ago
Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation
We study a sequential variance reduction technique for Monte Carlo estimation of functionals in Markov Chains. The method is based on designing sequential control variates using s...
Rémi Munos
NECO
2008
111views more  NECO 2008»
13 years 7 months ago
A Neural Network Model of the Eriksen Task: Reduction, Analysis, and Data Fitting
We analyze a neural network model of the Eriksen task, a twoalternative forced choice task in which subjects must correctly identify a central stimulus and disregard flankers that...
Yuan Sophie Liu, Philip Holmes, Jonathan D. Cohen
JMLR
2010
150views more  JMLR 2010»
13 years 2 months ago
Supervised Dimension Reduction Using Bayesian Mixture Modeling
We develop a Bayesian framework for supervised dimension reduction using a flexible nonparametric Bayesian mixture modeling approach. Our method retrieves the dimension reduction ...
Kai Mao, Feng Liang, Sayan Mukherjee
NPL
2002
145views more  NPL 2002»
13 years 7 months ago
Hybrid Feedforward Neural Networks for Solving Classification Problems
A novel multistage feedforward network is proposed for efficient solving of difficult classification tasks. The standard Radial Basis Functions (RBF) architecture is modified in or...
Iulian B. Ciocoiu