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164 Publications
2022 |
Published |
Conference Paper |
IST-REx-ID: 12299 |
Iofinova, E. B., Krumes, A., Kurtz, M., & Alistarh, D.-A. (2022). How well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01195
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| arXiv
2022 |
Published |
Conference Paper |
IST-REx-ID: 12780 |
Markov, I., Ramezanikebrya, H., & Alistarh, D.-A. (2022). CGX: Adaptive system support for communication-efficient deep learning. In Proceedings of the 23rd ACM/IFIP International Middleware Conference (pp. 241–254). Quebec, QC, Canada: Association for Computing Machinery. https://doi.org/10.1145/3528535.3565248
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| arXiv
2022 |
Research Data Reference |
IST-REx-ID: 13076 |
Postnikova, A., Koval, N., Nadiradze, G., & Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. Zenodo. https://doi.org/10.5281/ZENODO.5733408
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2022 |
Published |
Conference Paper |
IST-REx-ID: 11180 |
Postnikova, A., Koval, N., Nadiradze, G., & Alistarh, D.-A. (2022). Multi-queues can be state-of-the-art priority schedulers. In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 353–367). Seoul, Republic of Korea: Association for Computing Machinery. https://doi.org/10.1145/3503221.3508432
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| arXiv
2022 |
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Conference Paper |
IST-REx-ID: 11181 |
Brown, T. A., Sigouin, W., & Alistarh, D.-A. (2022). PathCAS: An efficient middle ground for concurrent search data structures. In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 385–399). Seoul, Republic of Korea: Association for Computing Machinery. https://doi.org/10.1145/3503221.3508410
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2022 |
Published |
Conference Paper |
IST-REx-ID: 11183 |
Nikabadi, A., & Korhonen, J. (2022). Beyond distributed subgraph detection: Induced subgraphs, multicolored problems and graph parameters. In Q. Bramas, V. Gramoli, & A. Milani (Eds.), 25th International Conference on Principles of Distributed Systems (Vol. 217). Strasbourg, France: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.OPODIS.2021.15
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2022 |
Published |
Conference Paper |
IST-REx-ID: 11184 |
Alistarh, D.-A., Gelashvili, R., & Rybicki, J. (2022). Fast graphical population protocols. In Q. Bramas, V. Gramoli, & A. Milani (Eds.), 25th International Conference on Principles of Distributed Systems (Vol. 217). Strasbourg, France: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.OPODIS.2021.14
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| arXiv
2022 |
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Conference Paper |
IST-REx-ID: 17059 |
Frantar, E., & Alistarh, D.-A. (2022). SPDY: Accurate pruning with speedup guarantees. In 39th International Conference on Machine Learning (Vol. 162, pp. 6726–6743). Baltimore, MD, United States: ML Research Press.
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2022 |
Published |
Conference Paper |
IST-REx-ID: 17087 |
Frantar, E., Singh, S. P., & Alistarh, D.-A. (2022). Optimal brain compression: A framework for accurate post-training quantization and pruning. In 36th Conference on Neural Information Processing Systems (Vol. 35). New Orleans, LA, United States: ML Research Press.
[Submitted Version]
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| arXiv
2022 |
Published |
Conference Paper |
IST-REx-ID: 17088 |
Kurtic, E., Campos, D., Nguyen, T., Frantar, E., Kurtz, M., Fineran, B., … Alistarh, D.-A. (2022). The optimal BERT surgeon: Scalable and accurate second-order pruning for large language models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 4163–4181). Abu Dhabi, United Arab Emirates: Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.279
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| arXiv
2022 |
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Journal Article |
IST-REx-ID: 8286 |
Alistarh, D.-A., Nadiradze, G., & Sabour, A. (2022). Dynamic averaging load balancing on cycles. Algorithmica. Virtual, Online; Germany: Springer Nature. https://doi.org/10.1007/s00453-021-00905-9
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| arXiv
2022 |
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Journal Article |
IST-REx-ID: 11420 |
Shevchenko, A., Kungurtsev, V., & Mondelli, M. (2022). Mean-field analysis of piecewise linear solutions for wide ReLU networks. Journal of Machine Learning Research. Journal of Machine Learning Research.
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| arXiv
2021 |
Published |
Conference Paper |
IST-REx-ID: 11452 |
Alimisis, F., Davies, P., Vandereycken, B., & Alistarh, D.-A. (2021). Distributed principal component analysis with limited communication. In Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems (Vol. 4, pp. 2823–2834). Virtual, Online: Neural Information Processing Systems Foundation.
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| arXiv
2021 |
Published |
Conference Paper |
IST-REx-ID: 11458 |
Krumes, A., Iofinova, E. B., Vladu, A., & Alistarh, D.-A. (2021). AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 8557–8570). Virtual, Online: Neural Information Processing Systems Foundation.
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| arXiv
2021 |
Published |
Conference Paper |
IST-REx-ID: 11463 |
Frantar, E., Kurtic, E., & Alistarh, D.-A. (2021). M-FAC: Efficient matrix-free approximations of second-order information. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 14873–14886). Virtual, Online: Neural Information Processing Systems Foundation.
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| arXiv
2021 |
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Conference Paper |
IST-REx-ID: 11464 |
Alistarh, D.-A., & Korhonen, J. (2021). Towards tight communication lower bounds for distributed optimisation. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 7254–7266). Virtual, Online: Neural Information Processing Systems Foundation.
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| arXiv
2021 |
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Conference Paper |
IST-REx-ID: 13147 |
Alimisis, F., Davies, P., & Alistarh, D.-A. (2021). Communication-efficient distributed optimization with quantized preconditioners. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 196–206). Virtual: ML Research Press.
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| arXiv
2021 |
Published |
Conference Paper |
IST-REx-ID: 15263 |
Alimisis, F., Orvieto, A., Becigneul, G., & Lucchi, A. (2021). Momentum improves optimization on Riemannian manifolds. In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (Vol. 130, pp. 1351–1359). San Diego, CA, United States; Virtual: ML Research Press.
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| arXiv
2021 |
Published |
Journal Article |
IST-REx-ID: 15267 |
Czumaj, A., & Davies, P. (2021). Exploiting spontaneous transmissions for broadcasting and leader election in radio networks. Journal of the ACM. Association for Computing Machinery. https://doi.org/10.1145/3446383
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| arXiv
2021 |
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Journal Article |
IST-REx-ID: 15271
Czumaj, A., Davies, P., & Parter, M. (2021). Simple, deterministic, constant-round coloring in congested clique and MPC. SIAM Journal on Computing. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/20m1366502
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