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118 Publications
2018 | Conference Paper | IST-REx-ID: 6031
Stojanov A, Smith TM, Alistarh D-A, Puschel M. Fast quantized arithmetic on x86: Trading compute for data movement. In: 2018 IEEE International Workshop on Signal Processing Systems. Vol 2018-October. IEEE; 2018. doi:10.1109/SiPS.2018.8598402
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2018 | Conference Paper | IST-REx-ID: 7123 | 
Alistarh D-A, Aspnes J, Gelashvili R. Space-optimal majority in population protocols. In: Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms. ACM; 2018:2221-2239. doi:10.1137/1.9781611975031.144
[Preprint]
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| arXiv
2018 | Conference Paper | IST-REx-ID: 6558 |
Alistarh D-A, Allen-Zhu Z, Li J. Byzantine stochastic gradient descent. In: Advances in Neural Information Processing Systems. Vol 2018. Neural Information Processing Systems Foundation; 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. The convergence of sparsified gradient methods. In: Advances in Neural Information Processing Systems 31. Vol Volume 2018. Neural Information Processing Systems Foundation; 2018:5973-5983.
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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. Towards unlicensed cellular networks in TV white spaces. In: Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies. ACM; 2017:2-14. doi:10.1145/3143361.3143367
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2017 | Conference Paper | IST-REx-ID: 791 |
Alistarh D-A, Kopinsky J, Li J, Nadiradze G. The power of choice in priority scheduling. In: Proceedings of the ACM Symposium on Principles of Distributed Computing. Vol Part F129314. ACM; 2017:283-292. doi:10.1145/3087801.3087810
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2017 | Conference Paper | IST-REx-ID: 431 |
Alistarh D-A, Grubic D, Li J, Tomioka R, Vojnović M. QSGD: Communication-efficient SGD via gradient quantization and encoding. In: Vol 2017. Neural Information Processing Systems Foundation; 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. ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. In: Proceedings of Machine Learning Research. Vol 70. ML Research Press; 2017:4035-4043.
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