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141 Publications

2023 | Published | Conference Paper | IST-REx-ID: 14458 | OA
SparseGPT: Massive language models can be accurately pruned in one-shot
E. Frantar, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 10323–10337.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge
M. Nikdan, T. Pegolotti, E.B. Iofinova, E. Kurtic, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 26215–26227.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2023 | Published | Conference Paper | IST-REx-ID: 14461 | OA
Quantized distributed training of large models with convergence guarantees
I. Markov, A. Vladu, Q. Guo, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 24020–24044.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2023 | Published | Conference Paper | IST-REx-ID: 14459 | OA
Fundamental limits of two-layer autoencoders, and achieving them with gradient methods
A. Shevchenko, K. Kögler, H. Hassani, M. Mondelli, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 31151–31209.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 12780 | OA
CGX: Adaptive system support for communication-efficient deep learning
I. Markov, H. Ramezanikebrya, D.-A. Alistarh, in:, Proceedings of the 23rd ACM/IFIP International Middleware Conference, Association for Computing Machinery, 2022, pp. 241–254.
[Published Version] View | Files available | DOI | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 17059 | OA
SPDY: Accurate pruning with speedup guarantees
E. Frantar, D.-A. Alistarh, in:, 39th International Conference on Machine Learning, ML Research Press, 2022, pp. 6726–6743.
[Published Version] View | Files available | WoS
 
2022 | Published | Conference Paper | IST-REx-ID: 17087 | OA
Optimal brain compression: A framework for accurate post-training quantization and pruning
E. Frantar, S.P. Singh, D.-A. Alistarh, in:, 36th Conference on Neural Information Processing Systems, ML Research Press, 2022.
[Submitted Version] View | Files available | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 17088 | OA
The optimal BERT surgeon: Scalable and accurate second-order pruning for large language models
E. Kurtic, D. Campos, T. Nguyen, E. Frantar, M. Kurtz, B. Fineran, M. Goin, D.-A. Alistarh, in:, Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2022, pp. 4163–4181.
[Published Version] View | Files available | DOI | arXiv
 
2022 | Published | Journal Article | IST-REx-ID: 8286 | OA
Dynamic averaging load balancing on cycles
D.-A. Alistarh, G. Nadiradze, A. Sabour, Algorithmica 84 (2022) 1007–1029.
[Published Version] View | Files available | DOI | WoS | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 12182 | OA
Brief announcement: Temporal locality in online algorithms
M. Pacut, M. Parham, J. Rybicki, S. Schmid, J. Suomela, A. Tereshchenko, in:, 36th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022.
[Published Version] View | Files available | DOI
 
2022 | Published | Conference Paper | IST-REx-ID: 12299 | OA
How well do sparse ImageNet models transfer?
E.B. Iofinova, A. Krumes, M. Kurtz, D.-A. Alistarh, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 12256–12266.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
2022 | Research Data Reference | IST-REx-ID: 13076 | OA
Multi-queues can be state-of-the-art priority schedulers
A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, (2022).
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
2022 | Published | Conference Paper | IST-REx-ID: 11180 | OA
Multi-queues can be state-of-the-art priority schedulers
A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2022, pp. 353–367.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 11181 | OA
PathCAS: An efficient middle ground for concurrent search data structures
T.A. Brown, W. Sigouin, D.-A. Alistarh, in:, Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2022, pp. 385–399.
[Published Version] View | Files available | DOI | WoS
 
2022 | Published | Conference Paper | IST-REx-ID: 11183 | OA
Beyond distributed subgraph detection: Induced subgraphs, multicolored problems and graph parameters
A. Nikabadi, J. Korhonen, in:, Q. Bramas, V. Gramoli, A. Milani (Eds.), 25th International Conference on Principles of Distributed Systems, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022.
[Published Version] View | Files available | DOI
 
2022 | Published | Conference Paper | IST-REx-ID: 11184 | OA
Fast graphical population protocols
D.-A. Alistarh, R. Gelashvili, J. Rybicki, in:, Q. Bramas, V. Gramoli, A. Milani (Eds.), 25th International Conference on Principles of Distributed Systems, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022.
[Published Version] View | Files available | DOI | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 11707 | OA
Local mending
A. Balliu, J. Hirvonen, D. Melnyk, D. Olivetti, J. Rybicki, J. Suomela, in:, M. Parter (Ed.), International Colloquium on Structural Information and Communication Complexity, Springer Nature, 2022, pp. 1–20.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2022 | Published | Conference Paper | IST-REx-ID: 11844 | OA
Near-optimal leader election in population protocols on graphs
D.-A. Alistarh, J. Rybicki, S. Voitovych, in:, Proceedings of the Annual ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2022, pp. 246–256.
[Published Version] View | Files available | DOI | arXiv
 
2022 | Published | Journal Article | IST-REx-ID: 11420 | OA
Mean-field analysis of piecewise linear solutions for wide ReLU networks
A. Shevchenko, V. Kungurtsev, M. Mondelli, Journal of Machine Learning Research 23 (2022) 1–55.
[Published Version] View | Files available | arXiv
 
2021 | Published | Journal Article | IST-REx-ID: 10180 | OA
Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
T. Hoefler, D.-A. Alistarh, T. Ben-Nun, N. Dryden, E.-A. Peste, Journal of Machine Learning Research 22 (2021) 1–124.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 

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