| 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 | 1772 | 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 | 561 | 2022 |
| 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 | 476 | 2017 |
| 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 | 465 | 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 | 223 | 2020 |
| 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 | 178 | 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 | 178 | 2018 |
| {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 | 169 | 2022 |
| 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 | 152 | 2021 |
| 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 | 148 | 2010 |
| Optimizing task recommendations in context-aware mobile crowdsourcing S Nath, M Goraczko, J Liu, A Mirhoseini US Patent 9,911,088, 2018 | 114 | 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 | 98 | 2020 |
| 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 | 88 | 2023 |
| 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 | 81 | 2013 |
| Flguard: Secure and private federated learning TD Nguyen, P Rieger, MH Yalame, H Möllering, H Fereidooni, S Marchal, ... Crytography and Security, 2021 | 71 | 2021 |
| Gap: Generalizable approximate graph partitioning framework A Nazi, W Hang, A Goldie, S Ravi, A Mirhoseini arXiv preprint arXiv:1903.00614, 2019 | 66 | 2019 |
| 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 | 60 | 2016 |
| Placement optimization with deep reinforcement learning A Goldie, A Mirhoseini Proceedings of the 2020 International Symposium on Physical Design, 3-7, 2020 | 52 | 2020 |
| Transferable graph optimizers for ml compilers Y Zhou, S Roy, A Abdolrashidi, D Wong, P Ma, Q Xu, H Liu, ... Advances in Neural Information Processing Systems 33, 13844-13855, 2020 | 51 | 2020 |
| Gdp: Generalized device placement for dataflow graphs Y Zhou, S Roy, A Abdolrashidi, D Wong, PC Ma, Q Xu, M Zhong, H Liu, ... arXiv preprint arXiv:1910.01578, 2019 | 44 | 2019 |
Anna GoldieGoogle DeepMind / StanfordDirección de correo verificada de google.com
