| QSGD: Communication-efficient SGD via gradient quantization and encoding D Alistarh, D Grubic, J Li, R Tomioka, M Vojnovic Advances in neural information processing systems 30, 2017 | 1894 | 2017 |
| Model compression via distillation and quantization A Polino, R Pascanu, D Alistarh ICLR 2018, 2018 | 823 | 2018 |
| Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks T Hoefler, D Alistarh, T Ben-Nun, N Dryden, A Peste Journal of Machine Learning Research 22 (241), 1-124, 2021 | 702 | 2021 |
| Gptq: Accurate post-training quantization for generative pre-trained transformers E Frantar, S Ashkboos, T Hoefler, D Alistarh arXiv preprint arXiv:2210.17323, 2022 | 586* | 2022 |
| The convergence of sparsified gradient methods D Alistarh, T Hoefler, M Johansson, N Konstantinov, S Khirirat, C Renggli Advances in Neural Information Processing Systems 31, 2018 | 561 | 2018 |
| Byzantine stochastic gradient descent D Alistarh, Z Allen-Zhu, J Li Advances in neural information processing systems 31, 2018 | 336 | 2018 |
| Sparsegpt: Massive language models can be accurately pruned in one-shot E Frantar, D Alistarh International Conference on Machine Learning, 10323-10337, 2023 | 291 | 2023 |
| ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning H Zhang, J Li, K Kara, D Alistarh, J Liu, C Zhang International Conference on Machine Learning, 4035-4043, 2017 | 244* | 2017 |
| Woodfisher: Efficient second-order approximation for neural network compression SP Singh, D Alistarh Advances in Neural Information Processing Systems 33, 18098-18109, 2020 | 158 | 2020 |
| Inducing and exploiting activation sparsity for fast inference on deep neural networks M Kurtz, J Kopinsky, R Gelashvili, A Matveev, J Carr, M Goin, W Leiserson, ... International Conference on Machine Learning, 5533-5543, 2020 | 155 | 2020 |
| The spraylist: A scalable relaxed priority queue D Alistarh, J Kopinsky, J Li, N Shavit Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of …, 2015 | 148 | 2015 |
| Time-space trade-offs in population protocols D Alistarh, J Aspnes, D Eisenstat, R Gelashvili, RL Rivest Proceedings of the twenty-eighth annual ACM-SIAM symposium on discrete …, 2017 | 140 | 2017 |
| SparCML: High-performance sparse communication for machine learning C Renggli, S Ashkboos, M Aghagolzadeh, D Alistarh, T Hoefler Proceedings of the International Conference for High Performance Computing …, 2019 | 139 | 2019 |
| 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 | 137 | 2022 |
| Fast and exact majority in population protocols D Alistarh, R Gelashvili, M Vojnović Proceedings of the 2015 ACM Symposium on Principles of Distributed Computing …, 2015 | 123 | 2015 |
| Space-optimal majority in population protocols D Alistarh, J Aspnes, R Gelashvili Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2018 | 120 | 2018 |
| 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 | 111 | 2023 |
| Polylogarithmic-time leader election in population protocols D Alistarh, R Gelashvili Automata, Languages, and Programming: 42nd International Colloquium, ICALP …, 2015 | 101 | 2015 |
| 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 | 96 | 2022 |
| FPGA-accelerated dense linear machine learning: A precision-convergence trade-off K Kara, D Alistarh, G Alonso, O Mutlu, C Zhang 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom …, 2017 | 92 | 2017 |
Jerry LiMicrosoft Research在 microsoft.com 的电子邮件经过验证
