| Attention is all you need A Vaswani, N Shazeer, N Parmar, J Uszkoreit, L Jones, AN Gomez, ... Advances in neural information processing systems 30, 2017 | 254458 | 2017 |
| Exploring the limits of transfer learning with a unified text-to-text transformer C Raffel, N Shazeer, A Roberts, K Lee, S Narang, M Matena, Y Zhou, W Li, ... Journal of machine learning research 21 (140), 1-67, 2020 | 32115 | 2020 |
| 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 | 9318 | 2023 |
| Outrageously large neural networks: The sparsely-gated mixture-of-experts layer N Shazeer, A Mirhoseini, K Maziarz, A Davis, Q Le, G Hinton, J Dean arXiv preprint arXiv:1701.06538, 2017 | 5659 | 2017 |
| Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity W Fedus, B Zoph, N Shazeer Journal of Machine Learning Research 23 (120), 1-39, 2022 | 4707 | 2022 |
| Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities G Comanici, E Bieber, M Schaekermann, I Pasupat, N Sachdeva, I Dhillon, ... arXiv preprint arXiv:2507.06261, 2025 | 2920 | 2025 |
| Scheduled sampling for sequence prediction with recurrent neural networks S Bengio, O Vinyals, N Jaitly, N Shazeer Advances in neural information processing systems 28, 2015 | 2862 | 2015 |
| Image transformer N Parmar, A Vaswani, J Uszkoreit, L Kaiser, N Shazeer, A Ku, D Tran International conference on machine learning, 4055-4064, 2018 | 2621 | 2018 |
| Gshard: Scaling giant models with conditional computation and automatic sharding D Lepikhin, HJ Lee, Y Xu, D Chen, O Firat, Y Huang, M Krikun, N Shazeer, ... arXiv preprint arXiv:2006.16668, 2020 | 2363 | 2020 |
| Lamda: Language models for dialog applications R Thoppilan, D De Freitas, J Hall, N Shazeer, A Kulshreshtha, HT Cheng, ... arXiv preprint arXiv:2201.08239, 2022 | 2290 | 2022 |
| Glu variants improve transformer N Shazeer arXiv preprint arXiv:2002.05202, 2020 | 2034 | 2020 |
| Exploring the limits of language modeling R Jozefowicz, O Vinyals, M Schuster, N Shazeer, Y Wu arXiv preprint arXiv:1602.02410, 2016 | 1586 | 2016 |
| Adafactor: Adaptive learning rates with sublinear memory cost N Shazeer, M Stern International conference on machine learning, 4596-4604, 2018 | 1414 | 2018 |
| Music transformer CZA Huang, A Vaswani, J Uszkoreit, N Shazeer, I Simon, C Hawthorne, ... arXiv preprint arXiv:1809.04281, 2018 | 1392 | 2018 |
| How much knowledge can you pack into the parameters of a language model? A Roberts, C Raffel, N Shazeer Proceedings of the 2020 conference on empirical methods in natural language …, 2020 | 1227 | 2020 |
| Generating wikipedia by summarizing long sequences PJ Liu, M Saleh, E Pot, B Goodrich, R Sepassi, L Kaiser, N Shazeer arXiv preprint arXiv:1801.10198, 2018 | 1202 | 2018 |
| Kaiser,., and Polosukhin, I.(2017). Attention is all you need A Vaswani, N Shazeer, N Parmar, J Uszkoreit, L Jones, AN Gomez Advances in neural information processing systems 30, 2017 | 992 | 2017 |
| Advances in neural information processing systems 30 A Vaswani, N Shazeer, N Parmar, J Uszkoreit, L Jones, AN Gomez, ... Curran Associates Inc, 2017 | 923 | 2017 |
| End-to-end text-dependent speaker verification G Heigold, I Moreno, S Bengio, N Shazeer 2016 IEEE international conference on acoustics, speech and signal …, 2016 | 858 | 2016 |
| Fast transformer decoding: One write-head is all you need N Shazeer arXiv preprint arXiv:1911.02150, 2019 | 798 | 2019 |