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» NINJA: Java for high performance numerical computing
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ICML
2006
IEEE
14 years 8 months ago
Bayesian regression with input noise for high dimensional data
This paper examines high dimensional regression with noise-contaminated input and output data. Goals of such learning problems include optimal prediction with noiseless query poin...
Jo-Anne Ting, Aaron D'Souza, Stefan Schaal
ICASSP
2009
IEEE
14 years 2 months ago
Shrinkage estimation of high dimensional covariance matrices
We address covariance estimation under mean-squared loss in the Gaussian setting. Specifically, we consider shrinkage methods which are suitable for high dimensional problems wit...
Yilun Chen, Ami Wiesel, Alfred O. Hero
IPPS
2006
IEEE
14 years 1 months ago
Algorithm-based checkpoint-free fault tolerance for parallel matrix computations on volatile resources
As the desire of scientists to perform ever larger computations drives the size of today’s high performance computers from hundreds, to thousands, and even tens of thousands of ...
Zizhong Chen, Jack Dongarra
RECOMB
2005
Springer
14 years 8 months ago
A Practical Approach to Significance Assessment in Alignment with Gaps
Abstract. Current numerical methods for assessing the statistical significance of local alignments with gaps are time consuming. Analytical solutions thus far have been limited to ...
Nicholas Chia, Ralf Bundschuh
VIIP
2001
13 years 9 months ago
Using Graphics Cards for Quantized FEM Computations
Graphics cards exercise increasingly more computing power and are highly optimized for high data transfer volumes. In contrast typical workstations perform badly when data exceeds...
Martin Rumpf, Robert Strzodka