| 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 | 4617 | 2023 |
| Finetuned language models are zero-shot learners J Wei, M Bosma, VY Zhao, K Guu, AW Yu, B Lester, N Du, AM Dai, QV Le arXiv preprint arXiv:2109.01652, 2021 | 2841 | 2021 |
| Generating sentences from a continuous space SR Bowman, L Vilnis, O Vinyals, AM Dai, R Jozefowicz, S Bengio Proceedings of the 20th SIGNLL Conference on Computational Natural Language …, 2016 | 2815 | 2016 |
| Natural questions: a benchmark for question answering research T Kwiatkowski, J Palomaki, O Redfield, M Collins, A Parikh, C Alberti, ... Transactions of the Association for Computational Linguistics 7, 453-466, 2019 | 2632 | 2019 |
| 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 | 2583 | 2024 |
| Scalable and accurate deep learning with electronic health records A Rajkomar, E Oren, K Chen, AM Dai, N Hajaj, M Hardt, PJ Liu, X Liu, ... NPJ digital medicine 1 (1), 1-10, 2018 | 2326 | 2018 |
| HyperNetworks D Ha, A Dai, QV Le Proceedings of the International Conference on Learning Representations, 2017 | 1703 | 2017 |
| Semi-supervised sequence learning AM Dai, QV Le Advances in neural information processing systems 28, 2015 | 1649 | 2015 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 1612 | 2023 |
| Adversarial Training Methods for Semi-Supervised Text Classification T Miyato, AM Dai, I Goodfellow Proceedings of the International Conference on Learning Representations, 2017 | 1326 | 2017 |
| Palm 2 technical report R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ... arXiv preprint arXiv:2305.10403, 2023 | 1265 | 2023 |
| 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 | 1028 | 2022 |
| Music transformer CZA Huang, A Vaswani, J Uszkoreit, N Shazeer, I Simon, C Hawthorne, ... arXiv preprint arXiv:1809.04281, 2018 | 929 | 2018 |
| Maskgan: better text generation via filling in the_ W Fedus, I Goodfellow, AM Dai arXiv preprint arXiv:1801.07736, 2018 | 630 | 2018 |
| Document embedding with paragraph vectors AM Dai, C Olah, QV Le NIPS 2014 Deep learning workshop, 2015 | 571 | 2015 |
| Glam: Efficient scaling of language models with mixture-of-experts N Du, Y Huang, AM Dai, S Tong, D Lepikhin, Y Xu, M Krikun, Y Zhou, ... International Conference on Machine Learning, 5547-5569, 2022 | 487 | 2022 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 412 | 2024 |
| Many paths to equilibrium: GANs do not need to decrease a divergence at every step W Fedus, M Rosca, B Lakshminarayanan, AM Dai, S Mohamed, ... arXiv preprint arXiv:1710.08446, 2017 | 260 | 2017 |
| Who said what: Modeling individual labelers improves classification M Guan, V Gulshan, A Dai, G Hinton Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 243 | 2018 |
| Gmail smart compose: Real-time assisted writing MX Chen, BN Lee, G Bansal, Y Cao, S Zhang, J Lu, J Tsay, Y Wang, ... Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 238 | 2019 |
Quoc V. LeResearch Scientist, GoogleVerified email at stanford.edu
