For further reading on sparse matrices and their applications in machine learning, consider the following papers from arXiv.org: "Efficient Sparse Matrix-Vector Multiplication on GPUs using the CSR ...

Abstract: Sparse-matrix dense-matrix multiplication (SpMM) receives one sparse matrix and one dense matrix as two inputs, and outputs one dense matrix as a result. It plays a vital role in various ...

Sparse data is still representing something within the variables. Missing data, however, means that the data points are unknown. Challenges in machine learning with sparse data. There are several ...

Sparse matrix by vector multiplication (SpMV) plays a pivotal role in machine learning and data mining. We propose and investigate an SpMV accelerator, specifically designed to accelerate the sparse ...

This project aims at providing a scalable approach to matrix multiplication, which is one of the most used step in machine learning. ##The Problem in Scalable Machine Learning. At the first sight, ...

Structured sparsity has emerged as a promising approach to streamline the complexity of modern Machine Learning (ML) applications and facilitate the handling of sparse data in hardware. Accelerating ...