| 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 | 2058 | 2016 |
| 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 | 1356 | 2018 |
| Semi-supervised sequence learning AM Dai, QV Le Advances in neural information processing systems 28, 2015 | 1227 | 2015 |
| HyperNetworks D Ha, A Dai, QV Le Proceedings of the International Conference on Learning Representations, 2017 | 948 | 2017 |
| 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 | 945 | 2019 |
| Adversarial Training Methods for Semi-Supervised Text Classification T Miyato, AM Dai, I Goodfellow Proceedings of the International Conference on Learning Representations, 2017 | 747 | 2017 |
| Maskgan: better text generation via filling in the_ W Fedus, I Goodfellow, AM Dai arXiv preprint arXiv:1801.07736, 2018 | 450 | 2018 |
| Document embedding with paragraph vectors AM Dai, C Olah, QV Le NIPS 2014 Deep learning workshop, 2015 | 450 | 2015 |
| Music transformer CZA Huang, A Vaswani, J Uszkoreit, N Shazeer, I Simon, C Hawthorne, ... arXiv preprint arXiv:1809.04281, 2018 | 419 | 2018 |
| 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 | 186 | 2017 |
| Learning longer-term dependencies in rnns with auxiliary losses T Trinh, A Dai, T Luong, Q Le International Conference on Machine Learning, 4965-4974, 2018 | 155 | 2018 |
| 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 | 151 | 2018 |
| 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 | 141 | 2021 |
| 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 | 128 | 2019 |
| Wearable sensors for Parkinson’s disease: which data are worth collecting for training symptom detection models L Lonini, A Dai, N Shawen, T Simuni, C Poon, L Shimanovich, ... NPJ digital medicine 1 (1), 1-8, 2018 | 116 | 2018 |
| Embedding text in hyperbolic spaces B Dhingra, CJ Shallue, M Norouzi, AM Dai, GE Dahl arXiv preprint arXiv:1806.04313, 2018 | 105 | 2018 |
| Palm: Scaling language modeling with pathways A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ... arXiv preprint arXiv:2204.02311, 2022 | 94 | 2022 |
| Learning the graphical structure of electronic health records with graph convolutional transformer E Choi, Z Xu, Y Li, M Dusenberry, G Flores, E Xue, A Dai Proceedings of the AAAI conference on artificial intelligence 34 (01), 606-613, 2020 | 85 | 2020 |
| Training independent subnetworks for robust prediction M Havasi, R Jenatton, S Fort, JZ Liu, J Snoek, B Lakshminarayanan, ... arXiv preprint arXiv:2010.06610, 2020 | 78 | 2020 |
| Analyzing the role of model uncertainty for electronic health records MW Dusenberry, D Tran, E Choi, J Kemp, J Nixon, G Jerfel, K Heller, ... Proceedings of the ACM Conference on Health, Inference, and Learning, 204-213, 2020 | 65 | 2020 |
Quoc V. LeResearch Scientist, Google Brain確認したメール アドレス: stanford.edu
