: 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...
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...
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 ...
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...
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...