| 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 | 18 | 2020 |
| 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 | 17 | 2021 |
| 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 | 16 | 2022 |
| SPDY: Accurate pruning with speedup guarantees E Frantar, D Alistarh International Conference on Machine Learning, 6726-6743, 2022 | 7 | 2022 |
| Optimal Brain Compression: A framework for accurate post-training quantization and pruning E Frantar, D Alistarh arXiv preprint arXiv:2208.11580, 2022 | 6 | 2022 |
| OPTQ: Accurate Quantization for Generative Pre-trained Transformers E Frantar, S Ashkboos, T Hoefler, D Alistarh The Eleventh International Conference on Learning Representations, 0 | 4* | |
| Massive Language Models Can Be Accurately Pruned in One-Shot E Frantar, D Alistarh arXiv preprint arXiv:2301.00774, 2023 | 2 | 2023 |
| ZipLM: Hardware-Aware Structured Pruning of Language Models E Kurtic, E Frantar, D Alistarh arXiv preprint arXiv:2302.04089, 2023 | | 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 | | 2022 |
| oViT: An Accurate Second-Order Pruning Framework for Vision Transformers D Kuznedelev, E Kurtic, E Frantar, D Alistarh arXiv preprint arXiv:2210.09223, 2022 | | 2022 |
