| Effective approaches to attention-based neural machine translation MT Luong, H Pham, CD Manning Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015 | 11302 | 2015 |
| Scaling up visual and vision-language representation learning with noisy text supervision C Jia, Y Yang, Y Xia, YT Chen, Z Parekh, H Pham, Q Le, YH Sung, Z Li, ... International conference on machine learning, 4904-4916, 2021 | 4040 | 2021 |
| Efficient neural architecture search via parameters sharing H Pham, M Guan, B Zoph, Q Le, J Dean International conference on machine learning, 4095-4104, 2018 | 3561 | 2018 |
| Neural combinatorial optimization with reinforcement learning I Bello, H Pham, QV Le, M Norouzi, S Bengio arXiv preprint arXiv:1611.09940, 2016 | 2093 | 2016 |
| Meta pseudo labels H Pham, Z Dai, Q Xie, QV Le Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 902 | 2021 |
| Device placement optimization with reinforcement learning A Mirhoseini, H Pham, QV Le, B Steiner, R Larsen, Y Zhou, N Kumar, ... International Conference on Machine Learning (ICML), 2017 | 553 | 2017 |
| Symbolic discovery of optimization algorithms X Chen, C Liang, D Huang, E Real, K Wang, H Pham, X Dong, T Luong, ... Advances in neural information processing systems 36, 49205-49233, 2023 | 478 | 2023 |
| Bilingual word representations with monolingual quality in mind MT Luong, H Pham, CD Manning Proceedings of the 1st workshop on vector space modeling for natural …, 2015 | 425 | 2015 |
| SwitchOut: an efficient data augmentation algorithm for neural machine translation X Wang, H Pham, Z Dai, G Neubig arXiv preprint arXiv:1808.07512, 2018 | 258 | 2018 |
| A Hierarchical Model for Device Placement A Mirhoseini, A Goldie, H Pham, B Steiner, QV Le, J Dean International Conference on Learning Representations (ICLR), 2019 | 219 | 2019 |
| Combined scaling for zero-shot transfer learning H Pham, Z Dai, G Ghiasi, K Kawaguchi, H Liu, AW Yu, J Yu, YT Chen, ... Neurocomputing 555, 126658, 2023 | 193 | 2023 |
| Effective approaches to attention-based neural machine translation. arXiv 2015 MT Luong, H Pham, CD Manning arXiv preprint arXiv:1508.04025, 2015 | 192 | 2015 |
| Doremi: Optimizing data mixtures speeds up language model pretraining SM Xie, H Pham, X Dong, N Du, H Liu, Y Lu, PS Liang, QV Le, T Ma, ... Advances in Neural Information Processing Systems 36, 69798-69818, 2023 | 146 | 2023 |
| Towards domain-agnostic contrastive learning V Verma, T Luong, K Kawaguchi, H Pham, Q Le International Conference on Machine Learning, 10530-10541, 2021 | 143 | 2021 |
| Multilingual neural machine translation with soft decoupled encoding X Wang, H Pham, P Arthur, G Neubig arXiv preprint arXiv:1902.03499, 2019 | 72 | 2019 |
| Optimizing data usage via differentiable rewards X Wang, H Pham, P Michel, A Anastasopoulos, J Carbonell, G Neubig International Conference on Machine Learning, 9983-9995, 2020 | 68 | 2020 |
| Autodropout: Learning dropout patterns to regularize deep networks H Pham, Q Le Proceedings of the AAAI conference on artificial intelligence 35 (11), 9351-9359, 2021 | 65 | 2021 |
| Learning distributed representations for multilingual text sequences H Pham, MT Luong, CD Manning Proceedings of the 1st Workshop on Vector Space Modeling for Natural …, 2015 | 61 | 2015 |
| Combined scaling for open-vocabulary image classification H Pham, Z Dai, G Ghiasi, K Kawaguchi, H Liu, AW Yu, J Yu, YT Chen, ... arXiv preprint arXiv:2111.10050 1 (2), 4, 2021 | 59 | 2021 |
| A tree-based decoder for neural machine translation X Wang, H Pham, P Yin, G Neubig arXiv preprint arXiv:1808.09374, 2018 | 59 | 2018 |
Quoc V. LeResearch Scientist, GoogleZweryfikowany adres z stanford.edu