As Transformer models continue to grow in size and complexity, numerous high-fidelity pruning methods have been proposed to mitigate the increasing parameter count. However, transforming these ...

Primary Algorithm : Algorithmically, Sparse-Sparse multiplication problems manifests itself in three possible forms:(a) Multiplication of a sparse matrix with a sparse diagonal, sparse block-diagonal, ...

Abstract: For accelerating the convergence of numerical computation of sparse matrices, the classical approach is to partition sparse matrices into block-diagonal structures by graph partitioning ...

Recently, a research team from the FhG Institute for Applied Solid State Physics and the University of Cologne has made significant progress in the field of quantum computing by proposing a new ...

Non-negative matrix factorization (NMF) is an effective local feature extraction algorithm with non-negative matrix constraints. In order to obtain a NMF-based algorithm with better clustering ...