| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2213 | 2023 |
| Towards a human-like open-domain chatbot D Adiwardana, MT Luong, DR So, J Hall, N Fiedel, R Thoppilan, Z Yang, ... arXiv preprint arXiv:2001.09977, 2020 | 1132 | 2020 |
| Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection Y Li, AW Yu, T Meng, B Caine, J Ngiam, D Peng, J Shen, Y Lu, D Zhou, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 396 | 2022 |
| Symbolic discovery of optimization algorithms X Chen, C Liang, D Huang, E Real, K Wang, H Pham, X Dong, T Luong, ... Advances in neural information processing systems 36, 2024 | 385 | 2024 |
| Larger language models do in-context learning differently J Wei, J Wei, Y Tay, D Tran, A Webson, Y Lu, X Chen, H Liu, D Huang, ... arXiv preprint arXiv:2303.03846, 2023 | 274 | 2023 |
| Transfer learning with neural automl C Wong, N Houlsby, Y Lu, A Gesmundo Advances in neural information processing systems 31, 2018 | 134 | 2018 |
| Doremi: Optimizing data mixtures speeds up language model pretraining SM Xie, H Pham, X Dong, N Du, H Liu, Y Lu, PS Liang, QV Le, T Ma, ... Advances in Neural Information Processing Systems 36, 2024 | 111 | 2024 |
| Simple synthetic data reduces sycophancy in large language models J Wei, D Huang, Y Lu, D Zhou, QV Le arXiv preprint arXiv:2308.03958, 2023 | 74 | 2023 |
| Symbol tuning improves in-context learning in language models J Wei, L Hou, A Lampinen, X Chen, D Huang, Y Tay, X Chen, Y Lu, ... arXiv preprint arXiv:2305.08298, 2023 | 60 | 2023 |
| Template-based structured document classification and extraction Y Sheng, Y Lu, J Xie, J Yang, LG Pueyo, J Lou, J Wendt US Patent 10,657,158, 2020 | 60 | 2020 |
| PyGlove: Symbolic programming for automated machine learning D Peng, X Dong, E Real, M Tan, Y Lu, G Bender, H Liu, A Kraft, C Liang, ... Advances in Neural Information Processing Systems 33, 96-108, 2020 | 35 | 2020 |
| Detecting LSB matching by characterizing the amplitude of histogram Y Gao, X Li, B Yang, Y Lu 2009 IEEE International Conference on Acoustics, Speech and Signal …, 2009 | 33 | 2009 |
| Brainformers: Trading simplicity for efficiency Y Zhou, N Du, Y Huang, D Peng, C Lan, D Huang, S Shakeri, D So, ... International Conference on Machine Learning, 42531-42542, 2023 | 21 | 2023 |
| Long-form factuality in large language models J Wei, C Yang, X Song, Y Lu, N Hu, D Tran, D Peng, R Liu, D Huang, ... arXiv preprint arXiv:2403.18802, 2024 | 14 | 2024 |
| Large language models as optimizers. 2023 C Yang, X Wang, Y Lu, H Liu, QV Le, D Zhou, X Chen Accessed on 1, 2023 | 13 | 2023 |
| Evolved optimizer for vision X Chen, C Liang, D Huang, E Real, Y Liu, K Wang, CJ Hsieh, Y Lu, QV Le First Conference on Automated Machine Learning (Late-Breaking Workshop), 2022 | 13 | 2022 |
| A secure steganography: noisy region embedding Y Lu, X Li, B Yang 2009 Fifth International Conference on Intelligent Information Hiding and …, 2009 | 12 | 2009 |
| TabNAS: Rejection sampling for neural architecture search on tabular datasets C Yang, G Bender, H Liu, PJ Kindermans, M Udell, Y Lu, QV Le, D Huang Advances in Neural Information Processing Systems 35, 11906-11917, 2022 | 9 | 2022 |
| Towards a human-like open-domain chatbot A Kulshreshtha, DDF Adiwardana, DR So, G Nemade, J Hall, N Fiedel, ... arXiv preprint arXiv:2001.09977, 2020 | 9 | 2020 |
| An end-to-end automl solution for tabular data at kaggledays Y Lu Google AI Blog, 2019 | 9 | 2019 |
Quoc V. LeResearch Scientist, GoogleVerified email at stanford.edu
