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MOR
2007
149views more  MOR 2007»
13 years 7 months ago
LP Rounding Approximation Algorithms for Stochastic Network Design
Real-world networks often need to be designed under uncertainty, with only partial information and predictions of demand available at the outset of the design process. The field ...
Anupam Gupta, R. Ravi, Amitabh Sinha
SIAMSC
2008
131views more  SIAMSC 2008»
13 years 7 months ago
Gramian-Based Model Reduction for Data-Sparse Systems
Model order reduction (MOR) is common in simulation, control and optimization of complex dynamical systems arising in modeling of physical processes and in the spatial discretizati...
Ulrike Baur, Peter Benner
ICIP
2009
IEEE
14 years 8 months ago
Reconstructing Ft-ir Spectroscopic Imaging Data With A Sparse Prior
Fourier Transform Infrared (FT-IR) spectroscopic imaging is a potentially valuable tool for diagnosing breast and prostate cancer, but its clinical deployment is limited due to lo...
TSP
2008
167views more  TSP 2008»
13 years 6 months ago
Multi-Task Learning for Analyzing and Sorting Large Databases of Sequential Data
A new hierarchical nonparametric Bayesian framework is proposed for the problem of multi-task learning (MTL) with sequential data. The models for multiple tasks, each characterize...
Kai Ni, John William Paisley, Lawrence Carin, Davi...
NIPS
2008
13 years 9 months ago
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
Many nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switc...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...