Markov I, Alimohammadi K, Frantar E, Alistarh D-A. L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning. In: Gibbons P, Pekhimenko G, De Sa C, eds. Proceedings of Machine Learning and Systems . Vol 6. Association for Computing Machinery; 2024.
LibreCat: https://research-explorer.ista.ac.at/record/17456
arXiv: 2210.17357

Frantar E. Compressing large neural networksf: Algorithms, systems and scaling laws. 2024. doi:10.15479/at:ista:17485
LibreCat: https://research-explorer.ista.ac.at/record/17485
DOI: 10.15479/at:ista:17485

Frantar E, Alistarh D-A. QMoE: Sub-1-bit compression of trillion parameter models. In: Gibbons P, Pekhimenko G, De Sa C, eds.  Proceedings of Machine Learning and Systems. Vol 6. ; 2024.
LibreCat: https://research-explorer.ista.ac.at/record/18061

Frantar E, Ruiz CR, Houlsby N, Alistarh D-A, Evci U. Scaling laws for sparsely-connected foundation models. In: The Twelfth International Conference on Learning Representations. ; 2024.
LibreCat: https://research-explorer.ista.ac.at/record/18062
arXiv: 2309.08520

Frantar E, Ashkboos S, Hoefler T, Alistarh D-A. OPTQ: Accurate post-training quantization for generative pre-trained transformers. In: 11th International Conference on Learning Representations . International Conference on Learning Representations; 2023.
LibreCat: https://research-explorer.ista.ac.at/record/17378

Frantar E, Alistarh D-A. SparseGPT: Massive language models can be accurately pruned in one-shot. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:10323-10337.
LibreCat: https://research-explorer.ista.ac.at/record/14458
arXiv: 2301.00774

Kurtic E, Campos D, Nguyen T, et al. 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. Association for Computational Linguistics; 2022:4163-4181. doi:10.18653/v1/2022.emnlp-main.279
LibreCat: https://research-explorer.ista.ac.at/record/17088
DOI: 10.18653/v1/2022.emnlp-main.279
arXiv: 2203.07259

Frantar E, Alistarh D-A. SPDY: Accurate pruning with speedup guarantees. In: 39th International Conference on Machine Learning. Vol 162. ML Research Press; 2022:6726-6743.
LibreCat: https://research-explorer.ista.ac.at/record/17059
WoS: 000922378801029

Frantar E, Singh SP, Alistarh D-A. Optimal brain compression: A framework for accurate post-training quantization and pruning. In: 36th Conference on Neural Information Processing Systems. Vol 35. ML Research Press; 2022.
LibreCat: https://research-explorer.ista.ac.at/record/17087
arXiv: 2208.11580

Frantar E, Kurtic E, Alistarh D-A. M-FAC: Efficient matrix-free approximations of second-order information. In: 35th Conference on Neural Information Processing Systems. Vol 34. Curran Associates; 2021:14873-14886.
LibreCat: https://research-explorer.ista.ac.at/record/11463
arXiv: 2010.08222

Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. On the sample complexity of adversarial multi-source PAC learning. In: Proceedings of the 37th International Conference on Machine Learning. Vol 119. ML Research Press; 2020:5416-5425.
LibreCat: https://research-explorer.ista.ac.at/record/8724
arXiv: 2002.10384