| Wide & deep learning for recommender systems HT Cheng, L Koc, J Harmsen, T Shaked, T Chandra, H Aradhye, ... Proceedings of the 1st workshop on deep learning for recommender systems, 7-10, 2016 | 3927 | 2016 |
| Palm 2 technical report R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ... arXiv preprint arXiv:2305.10403, 2023 | 1001 | 2023 |
| 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 | 888 | 2023 |
| Large scale distributed neural network training through online distillation R Anil, G Pereyra, AT Passos, R Ormandi, G Dahl, G Hinton Sixth International Conference on Learning Representations, 2018 | 474 | 2018 |
| Knowledge distillation: A good teacher is patient and consistent L Beyer, X Zhai, A Royer, L Markeeva, R Anil, A Kolesnikov Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 250 | 2022 |
| Efficiently Identifying Task Groupings for Multi-Task Learning C Fifty, E Amid, Z Zhao, T Yu, R Anil, C Finn 2021 Conference on Neural Information Processing Systems, Spotlight, 2021 | 239 | 2021 |
| Lingvo: a modular and scalable framework for sequence-to-sequence modeling J Shen, P Nguyen, Y Wu, Z Chen, MX Chen, Y Jia, A Kannan, T Sainath, ... arXiv preprint arXiv:1902.08295, 2019 | 202 | 2019 |
| Gemma: Open models based on gemini research and technology G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ... arXiv preprint arXiv:2403.08295, 2024 | 198 | 2024 |
| Tf-ranking: Scalable tensorflow library for learning-to-rank RK Pasumarthi, S Bruch, X Wang, C Li, M Bendersky, M Najork, J Pfeifer, ... Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 151 | 2019 |
| 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 | 141 | 2024 |
| Robust bi-tempered logistic loss based on bregman divergences E Amid, MK Warmuth, R Anil, T Koren 2019 Conference on Neural Information Processing Systems, 2019 | 128 | 2019 |
| Large-Scale Differentially Private BERT R Anil, B Ghazi, V Gupta, R Kumar, P Manurangsi Privacy Preserving Machine Learning, 2021 | 106 | 2021 |
| Scalable Second Order Optimization for Deep Learning R Anil, V Gupta, T Koren, K Regan, Y Singer arXiv preprint arXiv:2002.09018, 2020, 2020 | 106* | 2020 |
| Gemini: A family of highly capable multimodal models R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805 1, 2023 | 91 | 2023 |
| Memory-efficient adaptive optimization for large-scale learning R Anil, V Gupta, T Koren, Y Singer 2019 Conference on Neural Information Processing Systems, 2019 | 61* | 2019 |
| Sunipa Dev R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ... Jacob Devlin, Mark Díaz, Nan Du, Ethan Dyer, Vladimir Feinberg, Fangxiaoyu …, 2023 | 59 | 2023 |
| A large batch optimizer reality check: Traditional, generic optimizers suffice across batch sizes Z Nado, JM Gilmer, CJ Shallue, R Anil, GE Dahl arXiv preprint arXiv:2102.06356, 2021 | 38 | 2021 |
| Disentangling adaptive gradient methods from learning rates N Agarwal, R Anil, E Hazan, T Koren, C Zhang arXiv preprint arXiv:2002.11803, 2020 | 38 | 2020 |
| Wide and deep machine learning models T Shaked, R Anil, HB Aradhye, G Anderson, W Chai, ML Koc, J Harmsen, ... US Patent 10,762,422, 2020 | 32 | 2020 |
| On the factory floor: ML engineering for industrial-scale ads recommendation models R Anil, S Gadanho, D Huang, N Jacob, Z Li, D Lin, T Phillips, C Pop, ... arXiv preprint arXiv:2209.05310, 2022 | 24 | 2022 |
Ehsan AmidSenior Research Scientist at Google DeepMind在 google.com 的电子邮件经过验证
