| Sequence to sequence learning with neural networks I Sutskever, O Vinyals, QV Le arXiv preprint arXiv:1409.3215, 2014 | 28584 | 2014 |
| Efficientnet: Rethinking model scaling for convolutional neural networks M Tan, Q Le International Conference on Machine Learning, 6105-6114, 2019 | 26495 | 2019 |
| Distributed representations of sentences and documents Q Le, T Mikolov International conference on machine learning, 1188-1196, 2014 | 13461 | 2014 |
| Chain-of-thought prompting elicits reasoning in large language models J Wei, X Wang, D Schuurmans, M Bosma, F Xia, E Chi, QV Le, D Zhou Advances in neural information processing systems 35, 24824-24837, 2022 | 11878 | 2022 |
| Xlnet: Generalized autoregressive pretraining for language understanding Z Yang, Z Dai, Y Yang, J Carbonell, R Salakhutdinov, QV Le arXiv preprint arXiv:1906.08237, 2019 | 10734 | 2019 |
| Searching for mobilenetv3 A Howard, M Sandler, G Chu, LC Chen, B Chen, M Tan, W Wang, Y Zhu, ... Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 10058 | 2019 |
| Google's neural machine translation system: Bridging the gap between human and machine translation Y Wu, M Schuster, Z Chen, QV Le, M Norouzi, W Macherey, M Krikun, ... arXiv preprint arXiv:1609.08144, 2016 | 9656 | 2016 |
| Efficientdet: Scalable and efficient object detection M Tan, R Pang, QV Le Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 8154 | 2020 |
| Learning transferable architectures for scalable image recognition B Zoph, V Vasudevan, J Shlens, QV Le Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 7700 | 2018 |
| Neural architecture search with reinforcement learning B Zoph, QV Le arXiv preprint arXiv:1611.01578, 2016 | 7061 | 2016 |
| Searching for activation functions P Ramachandran, B Zoph, QV Le arXiv preprint arXiv:1710.05941, 2017 | 5288* | 2017 |
| Large scale Distributed Deep Networks AYN J. Dean, G.S. Corrado, R. Monga, K. Chen, M. Devin Advances In Neural Information Processing Systems 25 (7), 2012 | 5105 | 2012 |
| Autoaugment: Learning augmentation policies from data ED Cubuk, B Zoph, D Mane, V Vasudevan, QV Le arXiv preprint arXiv:1805.09501, 2018 | 4979* | 2018 |
| Transformer-xl: Attentive language models beyond a fixed-length context Z Dai, Z Yang, Y Yang, J Carbonell, QV Le, R Salakhutdinov arXiv preprint arXiv:1901.02860, 2019 | 4799 | 2019 |
| Electra: Pre-training text encoders as discriminators rather than generators K Clark, MT Luong, QV Le, CD Manning arXiv preprint arXiv:2003.10555, 2020 | 4730 | 2020 |
| Specaugment: A simple data augmentation method for automatic speech recognition DS Park, W Chan, Y Zhang, CC Chiu, B Zoph, ED Cubuk, QV Le arXiv preprint arXiv:1904.08779, 2019 | 4417 | 2019 |
| Randaugment: Practical automated data augmentation with a reduced search space ED Cubuk, B Zoph, J Shlens, QV Le Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 4361 | 2020 |
| Mnasnet: Platform-aware neural architecture search for mobile M Tan, B Chen, R Pang, V Vasudevan, M Sandler, A Howard, QV Le Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 3927 | 2019 |
| 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 | 3914 | 2021 |
| Efficientnetv2: Smaller models and faster training M Tan, Q Le International conference on machine learning, 10096-10106, 2021 | 3674 | 2021 |
Mingxing TanWaymo Research, Google Brainایمیل تأیید شده در google.com