High sparse Knowledge Graph is a key challenge to solve the Knowledge Graph Completion task. Due to the sparsity of the KGs, there are not enough first-order neighbors to learn the features of ...

Matrix-variate Gaussian graphical models (GGM) have been widely used for modeling matrix-variate data. Since the support of sparse precision matrix represents the conditional independence graph among ...

For a class of sparse random matrices of the form A n = ( ξ i,j δ i,j ) i,j=1 n where {ξi,j} are i.i.d. centered sub-Gaussian random variables of unit variance, and {δi,j} are i.i.d. Bernoulli random ...

A novel AI-acceleration paper presents a method to optimize sparse matrix multiplication for machine learning models, particularly focusing on structured sparsity. Structured sparsity involves a ...