Abstract: Matrix factorization is a fundamental characterization model in machine learning and is usually solved using mathematical decomposition reconstruction loss. However, matrix factorization is ...
Abstract: Matrix operators are fundamental to various applications, particularly in deep learning. While early models relied on dense operations, techniques like pruning have introduced sparsity, ...
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Institut de Química Computacional, Universitat de Girona, Girona (Spain) and Ronin Institute, Montclair, NJ, USA. An in-depth description of an apparently forgotten matrix operation, the reversal ...
With persistent fuel scarcity, Nigeria’s oil and gas sector stands at a critical crossroads, grappling with persistent challenges that demand immediate and decisive action. Despite the significant ...
Presenting an algorithm that solves linear systems with sparse coefficient matrices asymptotically faster than matrix multiplication for any ω > 2. Our algorithm can be viewed as an efficient, ...
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