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of 1 573
pro vyhledávání: '"Anderson, David G."'
Autor:
Banik, Riddhi, Neuman, Thomas G., Hao, Zerui, Al Sharabati, Miral, Zhao, Weibo, Anderson, David G., Przybycien, Todd, Kilduff, James (Chip), Belfort, Georges
Publikováno v:
In Separation and Purification Technology 19 February 2025 354 Part 7
Autor:
Anderson, David G., Gu, Ming
Publikováno v:
Proceedings of the 34th International Conference on Machine Learning, 70: 156-165 (2017)
Low-rank matrix approximation is a fundamental tool in data analysis for processing large datasets, reducing noise, and finding important signals. In this work, we present a novel truncated LU factorization called Spectrum-Revealing LU (SRLU) for eff
Externí odkaz:
http://arxiv.org/abs/1602.05950
Autor:
Alison K. Brown
Publikováno v:
Journal of the Royal Anthropological Institute. 20:377-378
Autor:
Khan, Taha, Lundgren, Lina E., Anderson, David G., Nowak, Irena, Dougherty, Mark, Verikas, Antanas, Pavel, Misha, Jimison, Holly, Nowaczyk, Slawomir, Aharonson, Vered
Publikováno v:
In Computer Speech & Language May 2020 61
Autor:
Polet, Sjoukje S., Anderson, David G., Koens, Lisette H., van Egmond, Martje E., Drost, Gea, Brusse, Esther, Willemsen, Michèl AAP., Sival, Deborah A., Brouwer, Oebele F., Kremer, Hubertus PH., de Vries, Jeroen J., Tijssen, Marina AJ., de Koning, Tom J.
Publikováno v:
In Parkinsonism and Related Disorders March 2020 72:44-48
Publikováno v:
In Neuropsychologia January 2020 136
Spectral graph sparsification has emerged as a powerful tool in the analysis of large-scale networks by reducing the overall number of edges, while maintaining a comparable graph Laplacian matrix. In this paper, we present an efficient algorithm for
Externí odkaz:
http://arxiv.org/abs/1410.4273
Akademický článek
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