1 Department of Computer Science, Nagoya Institute of Technology, Aichi, Japan 2 RIKEN Center for Advanced Intelligence Project, Tokyo, Japan In this paper, we propose a new unified optimization ...

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 ...

Abstract: Non-negative matrix factorization (NMF) is widely used for dimensionality reduction of large datasets and is an important feature extraction technique for source separation. However, NMF ...

ABSTRACT: Variational methods are highly valuable computational tools for solving high-dimensional quantum systems. In this paper, we explore the effectiveness of three variational methods: density ...

Welcome to the nlp-2.1-matrix-decomposition repository! This project provides a collection of algorithms for matrix decomposition, a fundamental concept in linear algebra. Whether you're working on ...

Abstract: Recommender systems (RSs) are useful technology that can alleviate the problem of overload of information provided to users. In this research, we build a new RS, and we name it ETagMF.

CHICAGO--(BUSINESS WIRE)--Matrix Executions, an agency-only broker dealer and trading technology provider, has enhanced its US listed options algorithm technology suite with new price discovery and ...

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 ...

Asynchronous Many-Task Systems and Applications: Second International Workshop, WAMTA 2024, Knoxville, TN, USA, February 14–16, 2024 The ubiquitous in-node heterogeneity of HPC and cloud computing ...

The goal in nonnegative matrix factorization (NMF) is to express, exactly or approximately, a given matrix as a product of two nonnegative matrices of smaller inner dimension. NMFs arise naturally in ...