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 ...
ABSTRACT: Truncated singular value decomposition (TSVD) and Golub-Kahan diagonalization are two elementary techniques for solving a least squares problem from a linear discrete ill-posed problems. For ...
Modern artificial intelligence and large language models are often discussed in terms of data scale and computational power. Beneath these systems, however, lies a mathematical foundation that enables ...
Abstract: Recommender Systems have gained attraction since last two decades owing to its popularity of providing customers with the information of their choice, items they might purchase, movies they ...
Hey there! I'm Aayush Khanna from Noida, Uttar Pradesh, India. I am a third year undergrad pursuing civil engineering at the Indian institute of Technology (BHU), Varanasi. I am interested in all ...
Medical imaging systems such as computed tomography (CT) and magnetic resonance imaging (MRI) are vital tools in clinical diagnosis and treatment planning. However, these modalities are inherently ...
Liam Gaughan is a film and TV writer at Collider. He has been writing film reviews and news coverage for ten years. Between relentlessly adding new titles to his watchlist and attending as many ...
ABSTRACT: The Kelly strategy is renowned for its theoretically optimal long-term growth, however, its practical application in financial markets is constrained by several limitations, including ...
Liam Gaughan is a film and TV writer at Collider. He has been writing film reviews and news coverage for ten years. Between relentlessly adding new titles to his watchlist and attending as many ...
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 ...
Each of these methods serves a different purpose, so choosing the right one depends on your specific dataset and goal. Whether you’re optimizing a linear regression model with PCA or exploring ...
Abstract: This paper studies various collaborative filtering item recommendation methods based on matrix factorization and clustering approaches. We develop six methods that are modified based on ...
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