| Sequence to sequence learning with neural networks I Sutskever, O Vinyals, QV Le arXiv preprint arXiv:1409.3215, 2014 | 27769 | 2014 |
| Efficientnet: Rethinking model scaling for convolutional neural networks M Tan, Q Le International Conference on Machine Learning, 6105-6114, 2019 | 23329 | 2019 |
| Distributed representations of sentences and documents Q Le, T Mikolov International conference on machine learning, 1188-1196, 2014 | 13064 | 2014 |
| 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 | 10034 | 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 | 9201 | 2016 |
| 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 | 8690 | 2019 |
| 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 | 8226* | 2022 |
| 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 | 7287 | 2018 |
| Efficientdet: Scalable and efficient object detection M Tan, R Pang, QV Le Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 7101 | 2020 |
| Neural architecture search with reinforcement learning B Zoph, QV Le arXiv preprint arXiv:1611.01578, 2016 | 6732 | 2016 |
| 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 | 4963 | 2012 |
| Searching for activation functions P Ramachandran, B Zoph, QV Le arXiv preprint arXiv:1710.05941, 2017 | 4722* | 2017 |
| Autoaugment: Learning augmentation policies from data ED Cubuk, B Zoph, D Mane, V Vasudevan, QV Le arXiv preprint arXiv:1805.09501, 2018 | 4591* | 2018 |
| Transformer-xl: Attentive language models beyond a fixed-length context Z Dai arXiv preprint arXiv:1901.02860, 2019 | 4409 | 2019 |
| Electra: Pre-training text encoders as discriminators rather than generators K Clark arXiv preprint arXiv:2003.10555, 2020 | 4294 | 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 | 4106 | 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 | 3917 | 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 | 3690 | 2019 |
| Regularized evolution for image classifier architecture search E Real, A Aggarwal, Y Huang, QV Le Proceedings of the aaai conference on artificial intelligence 33 (01), 4780-4789, 2019 | 3458 | 2019 |
| 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 | 3401 | 2018 |
Mingxing TanWaymo Research, Google Brain在 google.com 的电子邮件经过验证
