Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large, ...

This is a preview. Log in through your library . Abstract The sparsity constrained rank-one matrix approximation problem is a difficult mathematical optimization problem which arises in a wide array ...

Abstract Multipoint polynomial evaluation and interpolation are fundamental for modern symbolic and numerical computing. The known algorithms solve both problems over any field of constants in nearly ...

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REDWOOD CITY, Calif.--(BUSINESS WIRE)--Using algorithms derived from its neuroscience research, Numenta announced today it has achieved dramatic performance improvements on inference tasks in deep ...

Matrix multiplication is at the heart of many machine learning breakthroughs, and it just got faster—twice. Last week, DeepMind announced it discovered a more efficient way to perform matrix ...

The classic sparse matrix screen based on Jancaric and Kim (1991) and modified by Cudney et al (1994). Samples salts, polymers, organics and pH (see conditions). Helsinki Random II A combined sparse ...