3 Publications

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[3]
2023 | Conference Paper | IST-REx-ID: 14458 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.” In Proceedings of the 40th International Conference on Machine Learning, 202:10323–37. ML Research Press, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[2]
2021 | Conference Paper | IST-REx-ID: 11463 | OA
Frantar, Elias, Eldar Kurtic, and Dan-Adrian Alistarh. “M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.” In 35th Conference on Neural Information Processing Systems, 34:14873–86. Curran Associates, 2021.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[1]
2020 | Conference Paper | IST-REx-ID: 8724 | OA
Konstantinov, Nikola H, Elias Frantar, Dan-Adrian Alistarh, and Christoph Lampert. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” In Proceedings of the 37th International Conference on Machine Learning, 119:5416–25. ML Research Press, 2020.
[Published Version] View | Files available | arXiv
 

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3 Publications

Mark all

[3]
2023 | Conference Paper | IST-REx-ID: 14458 | OA
Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.” In Proceedings of the 40th International Conference on Machine Learning, 202:10323–37. ML Research Press, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[2]
2021 | Conference Paper | IST-REx-ID: 11463 | OA
Frantar, Elias, Eldar Kurtic, and Dan-Adrian Alistarh. “M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.” In 35th Conference on Neural Information Processing Systems, 34:14873–86. Curran Associates, 2021.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[1]
2020 | Conference Paper | IST-REx-ID: 8724 | OA
Konstantinov, Nikola H, Elias Frantar, Dan-Adrian Alistarh, and Christoph Lampert. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” In Proceedings of the 37th International Conference on Machine Learning, 119:5416–25. ML Research Press, 2020.
[Published Version] View | Files available | arXiv
 

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Citation Style: Chicago

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