The careful selection of energy-efficient components like voltage regulators plays a vital role in reducing energy use of a ...

Abstract: Matrix factorization is a fundamental characterization model in machine learning and is usually solved using mathematical decomposition reconstruction loss. However, matrix factorization is ...

There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed ...

This document is designed to help users quickly understand, use, and maintain the Python implementation of the Matrix-Sparsity-Based Pauli Decomposition (MSPD) algorithm. It specifies the function, ...

Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items. To predict scores for unrated items, matrix ...

Is this real life? Is this just fantasy? A growing number of scientists are suggesting that the idea that we are all living in a simulation may not be completely far-fetched. Simulation theory is the ...

Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States ...