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CORR
2010
Springer
107views Education» more  CORR 2010»
14 years 9 months ago
Fast GPGPU Data Rearrangement Kernels using CUDA
: Many high performance computing algorithms are bandwidth limited, hence the need for optimal data rearrangement kernels as well as their easy integration into the rest of the app...
Michael Bader, Hans-Joachim Bungartz, Dheevatsa Mu...
93
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ICASSP
2011
IEEE
14 years 1 months ago
Speaker recognition using multiple kernel learning based on conditional entropy minimization
We applied a multiple kernel learning (MKL) method based on information-theoretic optimization to speaker recognition. Most of the kernel methods applied to speaker recognition sy...
Tetsuji Ogawa, Hideitsu Hino, Nima Reyhani, Noboru...
COLT
2003
Springer
15 years 2 months ago
Maximum Margin Algorithms with Boolean Kernels
Recent work has introduced Boolean kernels with which one can learn linear threshold functions over a feature space containing all conjunctions of length up to k (for any 1 ≤ k ...
Roni Khardon, Rocco A. Servedio
NIPS
2007
14 years 11 months ago
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes
We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled da...
Ruslan Salakhutdinov, Geoffrey E. Hinton
PAMI
2008
144views more  PAMI 2008»
14 years 9 months ago
Twin Kernel Embedding
Visualization of non-vectorial objects is not easy in practice due to their lack of convenient vectorial representation. Representative approaches are Kernel PCA and Kernel Laplac...
Yi Guo, Junbin Gao, Paul W. Kwan