| Phi-3 technical report: A highly capable language model locally on your phone M Abdin, J Aneja, H Awadalla, A Awadallah, AA Awan, N Bach, A Bahree, ... arXiv preprint arXiv:2404.14219, 2024 | 514 | 2024 |
| How good are gpt models at machine translation? a comprehensive evaluation A Hendy, M Abdelrehim, A Sharaf, V Raunak, M Gabr, H Matsushita, ... arXiv preprint arXiv:2302.09210, 2023 | 366 | 2023 |
| Prediction of weather-induced airline delays based on machine learning algorithms S Choi, YJ Kim, S Briceno, D Mavris Digital Avionics Systems Conference (DASC), 2016 IEEE/AIAA 35th, 2016 | 228 | 2016 |
| A deep learning approach to flight delay prediction YJ Kim, S Choi, S Briceno, D Mavris 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), 2016 | 223 | 2016 |
| Taming sparsely activated transformer with stochastic experts S Zuo, X Liu, J Jiao, YJ Kim, H Hassan, R Zhang, T Zhao, J Gao arXiv preprint arXiv:2110.04260, 2021 | 101 | 2021 |
| A paradigm shift in machine translation: Boosting translation performance of large language models H Xu, YJ Kim, A Sharaf, HH Awadalla arXiv preprint arXiv:2309.11674, 2023 | 94 | 2023 |
| Contrastive preference optimization: Pushing the boundaries of llm performance in machine translation H Xu, A Sharaf, Y Chen, W Tan, L Shen, B Van Durme, K Murray, YJ Kim arXiv preprint arXiv:2401.08417, 2024 | 92 | 2024 |
| Scalable and efficient moe training for multitask multilingual models YJ Kim, AA Awan, A Muzio, AFC Salinas, L Lu, A Hendy, S Rajbhandari, ... arXiv preprint arXiv:2109.10465, 2021 | 77 | 2021 |
| Lower numerical precision deep learning inference and training A Rodriguez, E Segal, E Meiri, E Fomenko, YJ Kim, H Shen, B Ziv https://software.intel.com/en-us/articles/lower-numerical-precision-deep …, 2018 | 74 | 2018 |
| From Research to Production and Back: Ludicrously Fast Neural Machine Translation YJ Kim, M Junczys-Dowmunt, H Hassan, AF Aji, K Heafield, ... Proceedings of the 3rd Workshop on Neural Generation and Translation, 280-288, 2019 | 63 | 2019 |
| FastFormers: Highly efficient transformer models for natural language understanding YJ Kim, HH Awadalla arXiv preprint arXiv:2010.13382, 2020 | 58 | 2020 |
| Artificial neural network models for airport capacity prediction S Choi, YJ Kim Journal of Air Transport Management 97, 102146, 2021 | 40 | 2021 |
| Cost-sensitive prediction of airline delays using machine learning S Choi, YJ Kim, S Briceno, D Mavris 2017 IEEE/AIAA 36th Digital Avionics Systems, 2017 | 22 | 2017 |
| Gating dropout: Communication-efficient regularization for sparsely activated transformers R Liu, YJ Kim, A Muzio, H Hassan International Conference on Machine Learning, 13782-13792, 2022 | 20 | 2022 |
| Who Says Elephants Can't Run: Bringing Large Scale MoE Models into Cloud Scale Production YJ Kim, R Henry, R Fahim, HH Awadalla arXiv preprint arXiv:2211.10017, 2022 | 14 | 2022 |
| Finequant: Unlocking efficiency with fine-grained weight-only quantization for llms YJ Kim, R Henry, R Fahim, HH Awadalla arXiv preprint arXiv:2308.09723, 2023 | 13 | 2023 |
| Mixture of Quantized Experts (MoQE): Complementary Effect of Low-bit Quantization and Robustness YJ Kim, R Fahim, HH Awadalla arXiv preprint arXiv:2310.02410, 2023 | 9 | 2023 |
| Time-and space-parallel simulation of air traffic networks YJ Kim, D Mavris, R Fujimoto Simulation 95 (12), 1213-1228, 2019 | 8 | 2019 |
| Parallel Simulation of Agent-Based Model for Air Traffic Network YJ Kim, OJ Pinon-Fischer, DN Mavris AIAA Modeling and Simulation Technologies Conference, 2799, 2015 | 7 | 2015 |
| AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation G Jawahar, S Mukherjee, X Liu, YJ Kim, M Abdul-Mageed, ... arXiv preprint arXiv:2210.07535, 2022 | 6 | 2022 |
