A recent paper set the fastest record for multiplying two matrices. But it also marks the end of the line for a method researchers have relied on for decades to make improvements. For computer ...

Computer scientists are a demanding bunch. For them, it’s not enough to get the right answer to a problem — the goal, almost always, is to get the answer as efficiently as possible. Take the act of ...

Computer scientists have discovered a new way to multiply large matrices faster than ever before by eliminating a previously unknown inefficiency, reports Quanta Magazine. This could eventually ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Can artificial intelligence (AI) create its ...

Researchers claim to have developed a new way to run AI language models more efficiently by eliminating matrix multiplication from the process. This fundamentally redesigns neural network operations ...

Nearly all big science, machine learning, neural network, and machine vision applications employ algorithms that involve large matrix-matrix multiplication. But multiplying large matrices pushes the ...

The rationale for multiplicative models in the context of MTMM covariance matrices, as developed by Swain and Browne, is described and illustrated with several sets of empirical data. Comparisons with ...

Reduced rank approximation of matrices by Householder-Young methods is shown to be equivalent to fitting of a bilinear (multiplicative) model and to projection onto an optimally chosen subspace. The ...

Inverting a matrix is one of the most common tasks in data science and machine learning. In this article I explain why inverting a matrix is very difficult and present code that you can use as-is, or ...