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» Approximability of Probability Distributions
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JMLR
2010
202views more  JMLR 2010»
14 years 11 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
TCOM
2010
77views more  TCOM 2010»
14 years 11 months ago
Semi-Analytical Performance Prediction Methods for Iterative MMSE-IC Multiuser MIMO Joint Decoding
In this paper, two semi-analytical performance prediction methods are proposed and compared for multiuser MIMO transmission over block-fading multipath channels and iterative MMSE...
Raphaël Visoz, Antoine O. Berthet, Massinissa...
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TNN
2010
154views Management» more  TNN 2010»
14 years 11 months ago
Discriminative semi-supervised feature selection via manifold regularization
We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number ...
Zenglin Xu, Irwin King, Michael R. Lyu, Rong Jin
TSP
2010
14 years 11 months ago
Energy efficient state estimation with wireless sensors through the use of predictive power control and coding
We study state estimation via wireless sensors over fading channels. Packet loss probabilities depend upon time-varying channel gains, packet lengths and transmission power levels ...
Daniel E. Quevedo, Anders Ahlén, Jan &Oslas...
TNN
2011
132views more  TNN 2011»
14 years 10 months ago
Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals
—Prediction intervals (PIs) have been proposed in the literature to provide more information by quantifying the level of uncertainty associated to the point forecasts. Traditiona...
Abbas Khosravi, Saeid Nahavandi, Douglas C. Creigh...