| GPTQ: Accurate post-training compression for generative pretrained transformers E Frantar, S Ashkboos, T Hoefler, D Alistarh arXiv preprint arXiv:2210.17323 1, 2022 | 344* | 2022 |
| Sparsegpt: Massive language models can be accurately pruned in one-shot E Frantar, D Alistarh International Conference on Machine Learning, 10323-10337, 2023 | 180 | 2023 |
| Optimal brain compression: A framework for accurate post-training quantization and pruning E Frantar, D Alistarh Advances in Neural Information Processing Systems 35, 4475-4488, 2022 | 93 | 2022 |
| 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, ... arXiv preprint arXiv:2203.07259, 2022 | 70 | 2022 |
| Spqr: A sparse-quantized representation for near-lossless llm weight compression T Dettmers, R Svirschevski, V Egiazarian, D Kuznedelev, E Frantar, ... arXiv preprint arXiv:2306.03078, 2023 | 59 | 2023 |
| M-fac: Efficient matrix-free approximations of second-order information E Frantar, E Kurtic, D Alistarh Advances in Neural Information Processing Systems 34, 14873-14886, 2021 | 41 | 2021 |
| On the sample complexity of adversarial multi-source pac learning N Konstantinov, E Frantar, D Alistarh, C Lampert International Conference on Machine Learning, 5416-5425, 2020 | 25 | 2020 |
| SPDY: Accurate pruning with speedup guarantees E Frantar, D Alistarh International Conference on Machine Learning, 6726-6743, 2022 | 19 | 2022 |
| Ziplm: Hardware-aware structured pruning of language models E Kurtic, E Frantar, D Alistarh arXiv preprint arXiv:2302.04089 3 (7), 2023 | 15 | 2023 |
| Towards end-to-end 4-bit inference on generative large language models S Ashkboos, I Markov, E Frantar, T Zhong, X Wang, J Ren, T Hoefler, ... arXiv preprint arXiv:2310.09259, 2023 | 6 | 2023 |
| Sparse finetuning for inference acceleration of large language models E Kurtic, D Kuznedelev, E Frantar, M Goin, D Alistarh arXiv preprint arXiv:2310.06927, 2023 | 6 | 2023 |
| Qmoe: Practical sub-1-bit compression of trillion-parameter models E Frantar, D Alistarh arXiv preprint arXiv:2310.16795, 2023 | 5 | 2023 |
| Scaling laws for sparsely-connected foundation models E Frantar, C Riquelme, N Houlsby, D Alistarh, U Evci arXiv preprint arXiv:2309.08520, 2023 | 4 | 2023 |
| L-greco: An efficient and general framework for layerwise-adaptive gradient compression M Alimohammadi, I Markov, E Frantar, D Alistarh arXiv preprint arXiv:2210.17357, 2022 | 4 | 2022 |
| Extreme Compression of Large Language Models via Additive Quantization V Egiazarian, A Panferov, D Kuznedelev, E Frantar, A Babenko, D Alistarh arXiv preprint arXiv:2401.06118, 2024 | 3 | 2024 |
| JaxPruner: A concise library for sparsity research JH Lee, W Park, NE Mitchell, J Pilault, JSO Ceron, HB Kim, N Lee, ... Conference on Parsimony and Learning, 515-528, 2024 | 3 | 2024 |
| Accurate neural network pruning requires rethinking sparse optimization D Kuznedelev, E Kurtic, E Iofinova, E Frantar, A Peste, D Alistarh arXiv preprint arXiv:2308.02060, 2023 | 3 | 2023 |
| ovit: An accurate second-order pruning framework for vision transformers D Kuznedelev, E Kurtic, E Frantar, D Alistarh | 2 | 2022 |
| CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models D Kuznedelev, E Kurtić, E Frantar, D Alistarh Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
| QIGen: Generating Efficient Kernels for Quantized Inference on Large Language Models T Pegolotti, E Frantar, D Alistarh, M Püschel arXiv preprint arXiv:2307.03738, 2023 | 1* | 2023 |
