| Swift machine learning model serving scheduling: a region based reinforcement learning approach H Qin, S Zawad, Y Zhou, L Yang, D Zhao, F Yan Proceedings of the International Conference for High Performance Computing …, 2019 | 23 | 2019 |
| The age of correlated features in supervised learning based forecasting MKC Shisher, H Qin, L Yang, F Yan, Y Sun IEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops …, 2021 | 12 | 2021 |
| Reinforcement-learning-empowered MLaaS scheduling for serving intelligent internet of things H Qin, S Zawad, Y Zhou, S Padhi, L Yang, F Yan IEEE Internet of Things Journal 7 (7), 6325-6337, 2020 | 12 | 2020 |
| Nemo: an open-source transformer-supercharged benchmark for fine-grained wildfire smoke detection A Yazdi, H Qin, CB Jordan, L Yang, F Yan Remote Sensing 14 (16), 3979, 2022 | 7 | 2022 |
| DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales Z Yao, RY Aminabadi, O Ruwase, S Rajbhandari, X Wu, AA Awan, ... arXiv preprint arXiv:2308.01320, 2023 | 6 | 2023 |
| ZeRO++: Extremely Efficient Collective Communication for Giant Model Training G Wang, H Qin, SA Jacobs, C Holmes, S Rajbhandari, O Ruwase, F Yan, ... arXiv preprint arXiv:2306.10209, 2023 | 3 | 2023 |
| Simigrad: Fine-grained adaptive batching for large scale training using gradient similarity measurement H Qin, S Rajbhandari, O Ruwase, F Yan, L Yang, Y He Advances in Neural Information Processing Systems 34, 20531-20544, 2021 | 1 | 2021 |
| The Age of Correlated Features in Supervised Learning based Forecasting M Kamran Chowdhury Shisher, H Qin, L Yang, F Yan, Y Sun arXiv e-prints, arXiv: 2103.00092, 2021 | | 2021 |
