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 | 212 | 2023 |
Data augmentation for meta-learning R Ni, M Goldblum, A Sharaf, K Kong, T Goldstein International Conference on Machine Learning, 8152-8161, 2021 | 78 | 2021 |
Real-time multi-scale action detection from 3d skeleton data A Sharaf, M Torki, ME Hussein, M El-Saban 2015 IEEE Winter Conference on Applications of Computer Vision, 998-1005, 2015 | 73 | 2015 |
Meta-learning for few-shot NMT adaptation A Sharaf, H Hassan, H Daumé III arXiv preprint arXiv:2004.02745, 2020 | 32 | 2020 |
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 | 31 | 2023 |
Promoting fairness in learned models by learning to active learn under parity constraints A Sharaf, H Daume III, R Ni Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 22 | 2022 |
Active imitation learning with noisy guidance K Brantley, A Sharaf, H Daumé III arXiv preprint arXiv:2005.12801, 2020 | 19 | 2020 |
Meta-learning for contextual bandit exploration A Sharaf, H Daumé III arXiv preprint arXiv:1901.08159, 2019 | 17 | 2019 |
Leveraging gpt-4 for automatic translation post-editing V Raunak, A Sharaf, HH Awadallah, A Menezes arXiv preprint arXiv:2305.14878, 2023 | 14 | 2023 |
Structured prediction via learning to search under bandit feedback A Sharaf, H Daumé III Proceedings of the 2nd Workshop on Structured Prediction for Natural …, 2017 | 10 | 2017 |
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 | 9 | 2024 |
How good are GPT models at machine translation? A comprehensive evaluation. arXiv A Hendy, M Abdelrehim, A Sharaf, V Raunak, M Gabr, H Matsushita, ... arXiv preprint arXiv:2302.09210, 2023 | 9 | 2023 |
Kezhi Kong, and Tom Goldstein R Ni, M Goldblum, A Sharaf Data augmentation for meta-learning. ICML, 2021 | 6 | 2021 |
& Awadalla, HH (2023). 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, 0 | 6 | |
Residual loss prediction: Reinforcement learning with no incremental feedback H Daumé III, J Langford, A Sharaf International Conference on Learning Representations, 2018 | 5 | 2018 |
Strategies to improve few-shot learning for intent classification and slot-filling S Basu, A Sharaf, KIK Chong, A Fischer, V Rohra, M Amoake, ... Proceedings of the Workshop on Structured and Unstructured Knowledge …, 2022 | 4 | 2022 |
Semi-supervised few-shot intent classification and slot filling S Basu, A Sharaf, A Fischer, V Rohra, M Amoake, H El-Hammamy, ... arXiv preprint arXiv:2109.08754, 2021 | 4 | 2021 |
Meta-learning effective exploration strategies for contextual bandits A Sharaf, H Daumé III Proceedings of the AAAI Conference on Artificial Intelligence 35 (11), 9541-9548, 2021 | 4 | 2021 |
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task A Sharaf, S Feng, K Nguyen, K Brantley, H Daumé III arXiv preprint arXiv:1708.01318, 2017 | 4 | 2017 |
Random network distillation as a diversity metric for both image and text generation L Fowl, M Goldblum, A Gupta, A Sharaf, T Goldstein arXiv preprint arXiv:2010.06715, 2020 | 1 | 2020 |