| Original language | English |
|---|---|
| Pages (from-to) | 83-91 |
| Journal | Communications of the ACM |
| Volume | 62 |
| Issue number | 5 |
| Publication status | Published - 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 journal › Article › Research › peer-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 A, Boehm M, Haas PJ, Reiss FR, Reinwald B. Compressed linear algebra for declarative large-scale machine learning. Communications of the ACM. 2019;62(5):83-91.
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