Zachary G. Ives
- Abadi, M. et al. Tensorflow: A system for large-scale machine learning. OSDI, 16 (2016), 265--283. Google Scholar
Digital Library
- Ewen, S., Tzoumas, K., Kaufmann, M. and Markl, V. Spinning fast iterative data flows. In Proceedings of VLDB Endow. 5, 11 (2012), 1268--1279. Google ScholarDigital Library
- Ghoting, A. et al. SystemML: Declarative machine learning on MapReduce. ICDE. IEEE, 2011, 231--242. Google ScholarDigital Library
- Low, Y. et al. GraphLab: A new parallel framework for machine learning. In Proceedings of the Conference on Uncertainty in Artificial Intelligence. (Catalina Island, CA, July 2010). Google ScholarDigital Library
- Meng, X. et al. Mllib: Machine learning in Apache Spark. JMLR, 17, 1 (2016), 1235--1241. Google ScholarDigital Library
- Paszke, A. et al. Automatic differentiation. PyTorch, 2017.Google Scholar
- Team, T.T.D. et al. Theano: A python framework for fast computation of mathematical expressions. arXiv preprint arXiv:1605.02688, 2016.Google Scholar
- Zaharia, M., Chodhury, M., Franklin, M.J., Shenker, A. and Stoica, I. Spark: Cluster computing with working sets. HotCloud 10, 2010. Google ScholarDigital Library
Index Terms
- Technical perspective: Compressing matrices for large-scale machine learning
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