Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large ...

Best machine learning model for sparse data To help combat these issues that arise with sparse data machine learning, there are a few things to do. First, because of the noise in the model, it’s ...

Precision matrix is of significant importance in a wide range of applications in multivariate analysis. This paper considers adaptive minimax estimation of sparse precision matrices in the high ...

Real PIM systems can provide high levels of parallelism, large aggregate memory bandwidth and low memory access latency, thereby being a good fit to accelerate the widely-used, memory-bound Sparse ...

There is increasing interest in the study of community detection for sparse networks. Here, we propose a new method for detecting communities in sparse networks that uses the symmetrized Laplacian ...