| 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 | 5055 | 2018 |
| Neural architecture search with reinforcement learning B Zoph, QV Le arXiv preprint arXiv:1611.01578, 2016 | 4759 | 2016 |
| Searching for activation functions P Ramachandran, B Zoph, QV Le arXiv preprint arXiv:1710.05941, 2017 | 2493 | 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 | 2455 | 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 | 2367 | 2018 |
| 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 | 1806 | 2018 |
| 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 | 1612 | 2020 |
| 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 | 1519 | 2019 |
| Autoaugment: Learning augmentation policies from data ED Cubuk, B Zoph, D Mane, V Vasudevan, QV Le arXiv preprint arXiv:1805.09501, 2018 | 1251 | 2018 |
| 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 | 818 | 2019 |
| Transfer learning for low-resource neural machine translation B Zoph, D Yuret, J May, K Knight arXiv preprint arXiv:1604.02201, 2016 | 721 | 2016 |
| 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 | 660 | 2019 |
| 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 | 624 | 2018 |
| Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity W Fedus, B Zoph, N Shazeer J. Mach. Learn. Res 23, 1-40, 2021 | 539 | 2021 |
| Rethinking pre-training and self-training B Zoph, G Ghiasi, TY Lin, Y Cui, H Liu, ED Cubuk, Q Le Advances in neural information processing systems 33, 3833-3845, 2020 | 413 | 2020 |
| 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 | 404 | 2021 |
| Learning data augmentation strategies for object detection B Zoph, ED Cubuk, G Ghiasi, TY Lin, J Shlens, QV Le Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 392 | 2020 |
| Searching for efficient multi-scale architectures for dense image prediction LC Chen, M Collins, Y Zhu, G Papandreou, B Zoph, F Schroff, H Adam, ... Advances in neural information processing systems 31, 2018 | 381 | 2018 |
| Swish: a self-gated activation function P Ramachandran, B Zoph, QV Le arXiv preprint arXiv:1710.05941 7 (1), 5, 2017 | 381 | 2017 |
| Palm: Scaling language modeling with pathways A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ... arXiv preprint arXiv:2204.02311, 2022 | 357 | 2022 |
