Compressed linear algebra for declarative large-scale machine learning

Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald

Research output: Contribution to journalArticleResearchpeer-review

Original languageEnglish
Pages (from-to)83-91
JournalCommunications of the ACM
Volume62
Issue number5
Publication statusPublished - 2019

Cite this

Elgohary, A., Boehm, M., Haas, P. J., Reiss, F. R., & Reinwald, B. (2019). Compressed linear algebra for declarative large-scale machine learning. Communications of the ACM, 62(5), 83-91.

Compressed linear algebra for declarative large-scale machine learning. / Elgohary, Ahmed; Boehm, Matthias; Haas, Peter J.; Reiss, Frederick R.; Reinwald, Berthold.

In: Communications of the ACM, Vol. 62, No. 5, 2019, p. 83-91.

Research output: Contribution to journalArticleResearchpeer-review

Elgohary, A, Boehm, M, Haas, PJ, Reiss, FR & Reinwald, B 2019, 'Compressed linear algebra for declarative large-scale machine learning' Communications of the ACM, vol. 62, no. 5, pp. 83-91.
Elgohary, Ahmed ; Boehm, Matthias ; Haas, Peter J. ; Reiss, Frederick R. ; Reinwald, Berthold. / Compressed linear algebra for declarative large-scale machine learning. In: Communications of the ACM. 2019 ; Vol. 62, No. 5. pp. 83-91.
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