| Large batch optimization for deep learning: Training bert in 76 minutes Y You, J Li, S Reddi, J Hseu, S Kumar, S Bhojanapalli, X Song, J Demmel, ... arXiv preprint arXiv:1904.00962, 2019 | 794 | 2019 |
| Large batch training of convolutional networks Y You, I Gitman, B Ginsburg arXiv preprint arXiv:1708.03888, 2017 | 703 | 2017 |
| Imagenet training in minutes Y You, Z Zhang, CJ Hsieh, J Demmel, K Keutzer Proceedings of the 47th International Conference on Parallel Processing, 1-10, 2018 | 482 | 2018 |
| Scaling sgd batch size to 32k for imagenet training Y You, I Gitman, B Ginsburg arXiv preprint arXiv:1708.03888 6 (12), 6, 2017 | 369 | 2017 |
| Reducing BERT pre-training time from 3 days to 76 minutes Y You, J Li, J Hseu, X Song, J Demmel, CJ Hsieh arXiv preprint arXiv:1904.00962 12, 2019 | 100 | 2019 |
| Large-batch training for LSTM and beyond Y You, J Hseu, C Ying, J Demmel, K Keutzer, CJ Hsieh Proceedings of the International Conference for High Performance Computing …, 2019 | 91 | 2019 |
| Scaling deep learning on GPU and knights landing clusters Y You, A Buluç, J Demmel Proceedings of the International Conference for High Performance Computing …, 2017 | 91 | 2017 |
| Cafe: Learning to condense dataset by aligning features K Wang, B Zhao, X Peng, Z Zhu, S Yang, S Wang, G Huang, H Bilen, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 90 | 2022 |
| 100-epoch imagenet training with alexnet in 24 minutes Y You, Z Zhang, C Hsieh, J Demmel, K Keutzer arXiv preprint arXiv:1709.05011 8, 2017 | 74 | 2017 |
| Crafting better contrastive views for siamese representation learning X Peng, K Wang, Z Zhu, M Wang, Y You Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 73 | 2022 |
| Towards efficient and scalable sharpness-aware minimization Y Liu, S Mai, X Chen, CJ Hsieh, Y You Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 60 | 2022 |
| Fast deep neural network training on distributed systems and cloud TPUs Y You, Z Zhang, CJ Hsieh, J Demmel, K Keutzer IEEE Transactions on Parallel and Distributed Systems 30 (11), 2449-2462, 2019 | 51 | 2019 |
| Mic-svm: Designing a highly efficient support vector machine for advanced modern multi-core and many-core architectures Y You, SL Song, H Fu, A Marquez, MM Dehnavi, K Barker, KW Cameron, ... 2014 IEEE 28th International Parallel and Distributed Processing Symposium …, 2014 | 51 | 2014 |
| Asynchronous parallel greedy coordinate descent Y You, X Lian, J Liu, HF Yu, IS Dhillon, J Demmel, CJ Hsieh Advances in Neural Information Processing Systems, 4682-4690, 2016 | 49 | 2016 |
| CA-SVM: Communication-avoiding support vector machines on distributed systems Y You, J Demmel, K Czechowski, L Song, R Vuduc 2015 IEEE International Parallel and Distributed Processing Symposium, 847-859, 2015 | 46 | 2015 |
| Go wider instead of deeper F Xue, Z Shi, F Wei, Y Lou, Y Liu, Y You Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8779-8787, 2022 | 40 | 2022 |
| Colossal-ai: A unified deep learning system for large-scale parallel training S Li, H Liu, Z Bian, J Fang, H Huang, Y Liu, B Wang, Y You Proceedings of the 52nd International Conference on Parallel Processing, 766-775, 2023 | 38 | 2023 |
| Accurate, fast and scalable kernel ridge regression on parallel and distributed systems Y You, J Demmel, CJ Hsieh, R Vuduc Proceedings of the 2018 International Conference on Supercomputing, 307-317, 2018 | 37 | 2018 |
| Scaling support vector machines on modern HPC platforms Y You, H Fu, SL Song, A Randles, D Kerbyson, A Marquez, G Yang, ... Journal of Parallel and Distributed Computing 76, 16-31, 2015 | 37 | 2015 |
| PGAP-X: extension on pan-genome analysis pipeline Y Zhao, C Sun, D Zhao, Y Zhang, Y You, X Jia, J Yang, L Wang, J Wang, ... BMC genomics 19 (1), 115-124, 2018 | 34 | 2018 |
