| 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 | 2672 | 2018 |
| Neural architecture search with reinforcement learning B Zoph, QV Le arXiv preprint arXiv:1611.01578, 2016 | 2641 | 2016 |
| 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 | 1233 | 2018 |
| Searching for activation functions P Ramachandran, B Zoph, QV Le arXiv preprint arXiv:1710.05941, 2017 | 1140* | 2017 |
| Autoaugment: Learning augmentation strategies from data ED Cubuk*, B Zoph*, D Mane, V Vasudevan, QV Le Proceedings of the IEEE conference on computer vision and pattern …, 2019 | 1056* | 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 | 1015 | 2018 |
| 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 | 731 | 2019 |
| Transfer learning for low-resource neural machine translation B Zoph, D Yuret, J May, K Knight arXiv preprint arXiv:1604.02201, 2016 | 438 | 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 | 309 | 2018 |
| Randaugment: Practical automated data augmentation with a reduced search space ED Cubuk*, B Zoph*, J Shlens, QV Le arXiv preprint arXiv:1909.13719, 2019 | 308* | 2019 |
| Multi-source neural translation B Zoph, K Knight arXiv preprint arXiv:1601.00710, 2016 | 229 | 2016 |
| Searching for efficient multi-scale architectures for dense image prediction LC Chen, MD Collins, Y Zhu, G Papandreou, B Zoph, F Schroff, H Adam, ... arXiv preprint arXiv:1809.04184, 2018 | 220 | 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 | 193 | 2019 |
| Neural optimizer search with reinforcement learning I Bello, B Zoph, V Vasudevan, QV Le International Conference on Machine Learning, 459-468, 2017 | 191 | 2017 |
| 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 | 151 | 2019 |
| Learning data augmentation strategies for object detection B Zoph*, ED Cubuk*, G Ghiasi, TY Lin, J Shlens, QV Le arXiv preprint arXiv:1906.11172, 2019 | 119 | 2019 |
| Rethinking pre-training and self-training B Zoph*, G Ghiasi*, TY Lin*, Y Cui, H Liu, ED Cubuk, QV Le arXiv preprint arXiv:2006.06882, 2020 | 54 | 2020 |
| Intriguing properties of adversarial examples ED Cubuk, B Zoph, SS Schoenholz, QV Le arXiv preprint arXiv:1711.02846, 2017 | 49 | 2017 |
| Simple, fast noise-contrastive estimation for large rnn vocabularies B Zoph*, A Vaswani*, J May, K Knight Proceedings of the 2016 Conference of the North American Chapter of the …, 2016 | 40 | 2016 |
| Naive-student: Leveraging semi-supervised learning in video sequences for urban scene segmentation LC Chen, RG Lopes, B Cheng, MD Collins, ED Cubuk, B Zoph, H Adam, ... European Conference on Computer Vision, 695-714, 2020 | 27* | 2020 |
Quoc V. LeResearch Scientist, Google BrainVerified email at stanford.edu
