| 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 | 2466 | 2017 |
| Constitutional ai: Harmlessness from ai feedback Y Bai, S Kadavath, S Kundu, A Askell, J Kernion, A Jones, A Chen, ... arXiv preprint arXiv:2212.08073, 2022 | 1146 | 2022 |
| A graph placement methodology for fast chip design A Mirhoseini, A Goldie, M Yazgan, JW Jiang, E Songhori, S Wang, YJ Lee, ... Nature 594 (7862), 207-212, 2021 | 622 | 2021 |
| Device placement optimization with reinforcement learning A Mirhoseini, H Pham, QV Le, B Steiner, R Larsen, Y Zhou, N Kumar, ... International conference on machine learning, 2430-2439, 2017 | 537 | 2017 |
| {FLAME}: Taming backdoors in federated learning TD Nguyen, P Rieger, R De Viti, H Chen, BB Brandenburg, H Yalame, ... 31st USENIX Security Symposium (USENIX Security 22), 1415-1432, 2022 | 285 | 2022 |
| Representing long-range context for graph neural networks with global attention Z Wu, P Jain, M Wright, A Mirhoseini, JE Gonzalez, I Stoica Advances in Neural Information Processing Systems 34, 13266-13279, 2021 | 281 | 2021 |
| Chip placement with deep reinforcement learning A Mirhoseini, A Goldie, M Yazgan, J Jiang, E Songhori, S Wang, YJ Lee, ... arXiv preprint arXiv:2004.10746, 2020 | 257 | 2020 |
| SAFELearn: Secure aggregation for private federated learning H Fereidooni, S Marchal, M Miettinen, A Mirhoseini, H Möllering, ... 2021 IEEE Security and Privacy Workshops (SPW), 56-62, 2021 | 221 | 2021 |
| A hierarchical model for device placement A Mirhoseini, A Goldie, H Pham, B Steiner, QV Le, J Dean International Conference on Learning Representations, 2018 | 198 | 2018 |
| A unified framework for multimodal submodular integrated circuits trojan detection F Koushanfar, A Mirhoseini IEEE Transactions on Information Forensics and Security 6 (1), 162-174, 2010 | 151 | 2010 |
| The capacity for moral self-correction in large language models D Ganguli, A Askell, N Schiefer, TI Liao, K Lukošiūtė, A Chen, A Goldie, ... arXiv preprint arXiv:2302.07459, 2023 | 132 | 2023 |
| Optimizing task recommendations in context-aware mobile crowdsourcing S Nath, M Goraczko, J Liu, A Mirhoseini US Patent 9,911,088, 2018 | 127 | 2018 |
| Deep mixture of experts via shallow embedding X Wang, F Yu, L Dunlap, YA Ma, R Wang, A Mirhoseini, T Darrell, ... Uncertainty in artificial intelligence, 552-562, 2020 | 117 | 2020 |
| Measuring progress on scalable oversight for large language models SR Bowman, J Hyun, E Perez, E Chen, C Pettit, S Heiner, K Lukošiūtė, ... arXiv preprint arXiv:2211.03540, 2022 | 87 | 2022 |
| Idetic: A high-level synthesis approach for enabling long computations on transiently-powered ASICs A Mirhoseini, EM Songhori, F Koushanfar 2013 IEEE International Conference on Pervasive Computing and Communications …, 2013 | 86 | 2013 |
| FLGUARD: Secure and Private Federated Learning. TD Nguyen, P Rieger, H Yalame, H Möllering, H Fereidooni, S Marchal, ... IACR Cryptol. ePrint Arch. 2021, 25, 2021 | 83 | 2021 |
| Gap: Generalizable approximate graph partitioning framework A Nazi, W Hang, A Goldie, S Ravi, A Mirhoseini arXiv preprint arXiv:1903.00614, 2019 | 77 | 2019 |
| A full-stack search technique for domain optimized deep learning accelerators D Zhang, S Huda, E Songhori, K Prabhu, Q Le, A Goldie, A Mirhoseini Proceedings of the 27th ACM International Conference on Architectural …, 2022 | 63 | 2022 |
| Delight: Adding energy dimension to deep neural networks BD Rouhani, A Mirhoseini, F Koushanfar Proceedings of the 2016 International Symposium on Low Power Electronics and …, 2016 | 63 | 2016 |
| Large language monkeys: Scaling inference compute with repeated sampling B Brown, J Juravsky, R Ehrlich, R Clark, QV Le, C Ré, A Mirhoseini arXiv preprint arXiv:2407.21787, 2024 | 60 | 2024 |
Jeff DeanGoogle Chief Scientist, Google Research and Google DeepMindZweryfikowany adres z google.com
