Eldar Kurtic
7 Publications
2024 | Published | Conference Paper | IST-REx-ID: 15011 | 
Kurtic, Eldar, et al. “How to Prune Your Language Model: Recovering Accuracy on the ‘Sparsity May Cry’ Benchmark.” Proceedings of Machine Learning Research, vol. 234, ML Research Press, 2024, pp. 542–53.
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2024 | Published | Conference Paper | IST-REx-ID: 18975 |
Modoranu, Ionut-Vlad, et al. “Error Feedback Can Accurately Compress Preconditioners.” 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 35910–33.
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2024 | Published | Conference Paper | IST-REx-ID: 19510 |
Modoranu, Ionut-Vlad, et al. “MICROADAM: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence.” 38th Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.
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2023 | Published | Conference Paper | IST-REx-ID: 14460 |
Nikdan, Mahdi, et al. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 26215–27.
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2023 | Published | Conference Paper | IST-REx-ID: 13053 |
Krumes, Alexandra, et al. “CrAM: A Compression-Aware Minimizer.” 11th International Conference on Learning Representations , OpenReview, 2023.
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2022 | Published | Conference Paper | IST-REx-ID: 17088 |
Kurtic, Eldar, et al. “The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.” Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2022, pp. 4163–81, doi:10.18653/v1/2022.emnlp-main.279.
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2021 | Published | Conference Paper | IST-REx-ID: 11463 |
Frantar, Elias, et al. “M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.” 35th Conference on Neural Information Processing Systems, vol. 34, Neural Information Processing Systems Foundation, 2021, pp. 14873–86.
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7 Publications
2024 | Published | Conference Paper | IST-REx-ID: 15011 |
Kurtic, Eldar, et al. “How to Prune Your Language Model: Recovering Accuracy on the ‘Sparsity May Cry’ Benchmark.” Proceedings of Machine Learning Research, vol. 234, ML Research Press, 2024, pp. 542–53.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18975 |
Modoranu, Ionut-Vlad, et al. “Error Feedback Can Accurately Compress Preconditioners.” 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 35910–33.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19510 |
Modoranu, Ionut-Vlad, et al. “MICROADAM: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence.” 38th Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14460 |
Nikdan, Mahdi, et al. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 26215–27.
[Preprint]
View
| Download Preprint (ext.)
| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 13053 |
Krumes, Alexandra, et al. “CrAM: A Compression-Aware Minimizer.” 11th International Conference on Learning Representations , OpenReview, 2023.
[Published Version]
View
| Files available
| Download Published Version (ext.)
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 17088 |
Kurtic, Eldar, et al. “The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.” Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2022, pp. 4163–81, doi:10.18653/v1/2022.emnlp-main.279.
[Published Version]
View
| Files available
| DOI
| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 11463 |
Frantar, Elias, et al. “M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.” 35th Conference on Neural Information Processing Systems, vol. 34, Neural Information Processing Systems Foundation, 2021, pp. 14873–86.
[Published Version]
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| Download Published Version (ext.)
| arXiv