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118 Publications
2018 | Conference Paper | IST-REx-ID: 6031
Stojanov A, Smith TM, Alistarh D-A, Puschel M. 2018. Fast quantized arithmetic on x86: Trading compute for data movement. 2018 IEEE International Workshop on Signal Processing Systems. SiPS: Workshop on Signal Processing Systems vol. 2018–October, 8598402.
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| WoS
2018 | Conference Paper | IST-REx-ID: 7123 | 
Alistarh D-A, Aspnes J, Gelashvili R. 2018. Space-optimal majority in population protocols. Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms. SODA: Symposium on Discrete Algorithms, 2221–2239.
[Preprint]
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| arXiv
2018 | Conference Paper | IST-REx-ID: 6558 |
Alistarh D-A, Allen-Zhu Z, Li J. 2018. Byzantine stochastic gradient descent. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 2018, 4613–4623.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 6589 |
Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. 2018. The convergence of sparsified gradient methods. Advances in Neural Information Processing Systems 31. NeurIPS: Conference on Neural Information Processing Systems vol. Volume 2018, 5973–5983.
[Preprint]
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| arXiv
2017 | Conference Paper | IST-REx-ID: 487
Baig G, Radunovic B, Alistarh D-A, Balkwill M, Karagiannis T, Qiu L. 2017. Towards unlicensed cellular networks in TV white spaces. Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies. CoNEXT: Conference on emerging Networking EXperiments and Technologies, 2–14.
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2017 | Conference Paper | IST-REx-ID: 791 |
Alistarh D-A, Kopinsky J, Li J, Nadiradze G. 2017. The power of choice in priority scheduling. Proceedings of the ACM Symposium on Principles of Distributed Computing. PODC: Principles of Distributed Computing vol. Part F129314, 283–292.
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2017 | Conference Paper | IST-REx-ID: 431 |
Alistarh D-A, Grubic D, Li J, Tomioka R, Vojnović M. 2017. QSGD: Communication-efficient SGD via gradient quantization and encoding. NIPS: Neural Information Processing System, Advances in Neural Information Processing Systems, vol. 2017, 1710–1721.
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| arXiv
2017 | Conference Paper | IST-REx-ID: 432 |
Zhang H, Li J, Kara K, Alistarh D-A, Liu J, Zhang C. 2017. ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. Proceedings of Machine Learning Research. ICML: International Conference on Machine Learning, PMLR Press, vol. 70, 4035–4043.
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