Multicore processors are marking the beginning of a new era of computing where massive parallelism is available and necessary. Slightly slower but easy to parallelize kernels are ...
We develop a hierarchical matrix construction algorithm using matrixvector multiplications, based on the randomized singular value decomposition of low-rank matrices. The algorith...
We propose a general and efficient algorithm for learning low-rank matrices. The proposed algorithm converges super-linearly and can keep the matrix to be learned in a compact fac...
In this paper, we rst present a theorem that HOSVD-based representation of high-order tensor data provides a robust framework that can be used for a uni ed representation of the H...
Background: Multiple sequence alignments are a fundamental tool for the comparative analysis of proteins and nucleic acids. However, large data sets are no longer manageable for v...
Alberto I. Roca, Albert E. Almada, Aaron C. Abajia...