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

Collaborative filtering (CF) over ordinal feedback is naturally organized as a problem of matrix completion, where the input consists of a partially observed user-item interaction matrix. Maximum ...

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, ...

ABSTRACT: The offline course “Home Plant Health Care,” which is available to the senior population, serves as the study object for this paper. Learn how to use artificial intelligence technologies to ...

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 ...

Generative A.I. chatbots are going down conspiratorial rabbit holes and endorsing wild, mystical belief systems. For some people, conversations with the technology can deeply distort reality. By ...

Set between The Matrix and The Matrix Reloaded, Kid’s Story focuses upon a teenage boy named Michael Karl Popper (voiced in the English dub by Watson) who has long sensed something being off in the ...

Graph clustering is a fundamental task in network analysis, aimed at uncovering meaningful groups of nodes based on structural and attribute-based similarities. Traditional Nonnegative Matrix ...

As Machine Learning (ML) applications rapidly grow, concerns about adversarial attacks compromising their reliability have gained significant attention. One unsupervised ML method known for its ...