| XLNet: Generalized Autoregressive Pretraining for Language Understanding Z Yang arXiv preprint arXiv:1906.08237, 2019 | 10787 | 2019 |
| Transformer-xl: Attentive language models beyond a fixed-length context Z Dai arXiv preprint arXiv:1901.02860, 2019 | 4684 | 2019 |
| Unsupervised data augmentation for consistency training Q Xie, Z Dai, E Hovy, T Luong, Q Le Advances in neural information processing systems 33, 6256-6268, 2020 | 2692 | 2020 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2517 | 2023 |
| Coatnet: Marrying convolution and attention for all data sizes Z Dai, H Liu, QV Le, M Tan Advances in neural information processing systems 34, 3965-3977, 2021 | 1453 | 2021 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 963 | 2024 |
| Meta pseudo labels H Pham, Z Dai, Q Xie, QV Le Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 875 | 2021 |
| Simvlm: Simple visual language model pretraining with weak supervision Z Wang, J Yu, AW Yu, Z Dai, Y Tsvetkov, Y Cao arXiv preprint arXiv:2108.10904, 2021 | 870 | 2021 |
| Pay attention to mlps H Liu, Z Dai, D So, QV Le Advances in neural information processing systems 34, 9204-9215, 2021 | 681 | 2021 |
| Characterizing and avoiding negative transfer Z Wang, Z Dai, B Póczos, J Carbonell Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 609 | 2019 |
| Good semi-supervised learning that requires a bad gan Z Dai, Z Yang, F Yang, WW Cohen, RR Salakhutdinov Advances in neural information processing systems 30, 2017 | 589 | 2017 |
| Breaking the softmax bottleneck: A high-rank RNN language model Z Yang, Z Dai, R Salakhutdinov, WW Cohen arXiv preprint arXiv:1711.03953, 2017 | 437 | 2017 |
| Controllable invariance through adversarial feature learning Q Xie, Z Dai, Y Du, E Hovy, G Neubig Advances in neural information processing systems 30, 2017 | 312 | 2017 |
| Transformer quality in linear time W Hua, Z Dai, H Liu, Q Le International conference on machine learning, 9099-9117, 2022 | 274 | 2022 |
| Unsupervised data augmentation Q Xie, Z Dai, E Hovy, MT Luong, QV Le arXiv preprint arXiv:1904.12848 2 (6), 7, 2019 | 271 | 2019 |
| Funnel-transformer: Filtering out sequential redundancy for efficient language processing Z Dai, G Lai, Y Yang, Q Le Advances in neural information processing systems 33, 4271-4282, 2020 | 255 | 2020 |
| SwitchOut: an efficient data augmentation algorithm for neural machine translation X Wang, H Pham, Z Dai, G Neubig arXiv preprint arXiv:1808.07512, 2018 | 253 | 2018 |
| Combined scaling for zero-shot transfer learning H Pham, Z Dai, G Ghiasi, K Kawaguchi, H Liu, AW Yu, J Yu, YT Chen, ... Neurocomputing 555, 126658, 2023 | 189 | 2023 |
| Cfo: Conditional focused neural question answering with large-scale knowledge bases Z Dai, L Li, W Xu arXiv preprint arXiv:1606.01994, 2016 | 187 | 2016 |
| Searching for efficient transformers for language modeling D So, W Mańke, H Liu, Z Dai, N Shazeer, QV Le Advances in neural information processing systems 34, 6010-6022, 2021 | 172 | 2021 |
