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LibreCat: https://research-explorer.ista.ac.at/record/7123
DOI: 10.1137/1.9781611975031.144
WoS: 000483921200145
arXiv: 1704.04947

Alistarh, Dan-Adrian, et al. “Byzantine Stochastic Gradient Descent.” Advances in Neural Information Processing Systems, vol. 2018, Neural Information Processing Systems Foundation, 2018, pp. 4613–23.
LibreCat: https://research-explorer.ista.ac.at/record/6558
WoS: 000461823304061
arXiv: 1803.08917

Alistarh, Dan-Adrian, et al. “The Convergence of Sparsified Gradient Methods.” Advances in Neural Information Processing Systems 31, vol. Volume 2018, Neural Information Processing Systems Foundation, 2018, pp. 5973–83.
LibreCat: https://research-explorer.ista.ac.at/record/6589
WoS: 000461852000047
arXiv: 1809.10505

Baig, Ghufran, et al. “Towards Unlicensed Cellular Networks in TV White Spaces.”10.1145/3143361.3143367__Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies, ACM, 2017, pp. 2–14, doi:10.1145/3143361.3143367.
LibreCat: https://research-explorer.ista.ac.at/record/487
DOI: 10.1145/3143361.3143367

Alistarh, Dan-Adrian, et al. “The Power of Choice in Priority Scheduling.”10.1145/3087801.3087810__Proceedings of the ACM Symposium on Principles of Distributed Computing, vol. Part 9314, ACM, 2017, pp. 283–92, doi:10.1145/3087801.3087810.
LibreCat: https://research-explorer.ista.ac.at/record/791
DOI: 10.1145/3087801.3087810
WoS: 000462995000035

Alistarh, Dan-Adrian, et al. QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding. Vol. 2017, Neural Information Processing Systems Foundation, 2017, pp. 1710–21.
LibreCat: https://research-explorer.ista.ac.at/record/431
arXiv: 1610.02132

Zhang, Hantian, et al. “ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning.” Proceedings of Machine Learning Research, vol. 70, ML Research Press, 2017, pp. 4035–43.
LibreCat: https://research-explorer.ista.ac.at/record/432