| 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 | 6659 | 2018 |
| Neural architecture search with reinforcement learning B Zoph, QV Le arXiv preprint arXiv:1611.01578, 2016 | 6079 | 2016 |
| Searching for activation functions P Ramachandran, B Zoph, QV Le arXiv preprint arXiv:1710.05941, 2017 | 3708 | 2017 |
| 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 | 3707 | 2019 |
| Palm: Scaling language modeling with pathways A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ... Journal of Machine Learning Research 24 (240), 1-113, 2023 | 3344 | 2023 |
| 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 | 3140 | 2020 |
| 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 | 3106 | 2018 |
| Autoaugment: Learning augmentation strategies from data ED Cubuk, B Zoph, D Mane, V Vasudevan, QV Le Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 2357 | 2019 |
| Progressive neural architecture search C Liu, B Zoph, M Neumann, J Shlens, W Hua, LJ Li, L Fei-Fei, A Yuille, ... Proceedings of the European conference on computer vision (ECCV), 19-34, 2018 | 2254 | 2018 |
| Autoaugment: Learning augmentation policies from data ED Cubuk, B Zoph, D Mane, V Vasudevan, QV Le arXiv preprint arXiv:1805.09501, 2018 | 1804 | 2018 |
| Scaling instruction-finetuned language models HW Chung, L Hou, S Longpre, B Zoph, Y Tay, W Fedus, Y Li, X Wang, ... Journal of Machine Learning Research 25 (70), 1-53, 2024 | 1525 | 2024 |
| Emergent abilities of large language models J Wei, Y Tay, R Bommasani, C Raffel, B Zoph, S Borgeaud, D Yogatama, ... arXiv preprint arXiv:2206.07682, 2022 | 1380 | 2022 |
| Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity W Fedus, B Zoph, N Shazeer Journal of Machine Learning Research 23 (120), 1-39, 2022 | 1277 | 2022 |
| Attention augmented convolutional networks I Bello, B Zoph, A Vaswani, J Shlens, QV Le Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 1211 | 2019 |
| Augmix: A simple data processing method to improve robustness and uncertainty D Hendrycks, N Mu, ED Cubuk, B Zoph, J Gilmer, B Lakshminarayanan arXiv preprint arXiv:1912.02781, 2019 | 1186 | 2019 |
| Simple copy-paste is a strong data augmentation method for instance segmentation G Ghiasi, Y Cui, A Srinivas, R Qian, TY Lin, ED Cubuk, QV Le, B Zoph Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 923 | 2021 |
| Transfer learning for low-resource neural machine translation B Zoph, D Yuret, J May, K Knight arXiv preprint arXiv:1604.02201, 2016 | 901 | 2016 |
| Understanding and simplifying one-shot architecture search G Bender, PJ Kindermans, B Zoph, V Vasudevan, Q Le International conference on machine learning, 550-559, 2018 | 828 | 2018 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 724 | 2022 |
| Gpt-4 technical report J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... arXiv preprint arXiv:2303.08774, 2023 | 720 | 2023 |
