| Gemini: a family of highly capable multimodal models G team https://storage.googleapis.com/deepmind-media/gemini/gemini_1_report.pdf, 2023 | 1490 | 2023 |
| Carbon emissions and large neural network training D Patterson, J Gonzalez, Q Le, C Liang, LM Munguia, D Rothchild, D So, ... arXiv preprint arXiv:2104.10350, 2021 | 734 | 2021 |
| The evolved transformer D So, C Liang, Q Le International conference on machine learning, 5877-5886, 2019 | 544 | 2019 |
| Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision C Liang, J Berant, Q Le, KD Forbus, N Lao ACL 2017, 55th Annual Meeting of the Association for Computational …, 2017 | 461 | 2017 |
| Symbolic Discovery of Optimization Algorithms X Chen*, C Liang*, D Huang, E Real, K Wang, Y Liu, H Pham, X Dong, ... arXiv preprint arXiv:2302.06675, 2023 | 328 | 2023 |
| Automl-zero: Evolving machine learning algorithms from scratch E Real*, C Liang*, D So, Q Le International Conference on Machine Learning, 8007-8019, 2020 | 325 | 2020 |
| The carbon footprint of machine learning training will plateau, then shrink D Patterson, J Gonzalez, U Hölzle, Q Le, C Liang, LM Munguia, ... Computer 55 (7), 18-28, 2022 | 289 | 2022 |
| Memory augmented policy optimization for program synthesis and semantic parsing C Liang, M Norouzi, J Berant, QV Le, N Lao Advances in Neural Information Processing Systems 31, 2018 | 169* | 2018 |
| Definition modeling: Learning to define word embeddings in natural language T Noraset, C Liang, L Birnbaum, D Downey Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 139 | 2017 |
| Neural symbolic reader: Scalable integration of distributed and symbolic representations for reading comprehension X Chen, C Liang, AW Yu, D Zhou, D Song, QV Le International Conference on Learning Representations, 2019 | 121 | 2019 |
| Learning to generalize from sparse and underspecified rewards R Agarwal, C Liang, D Schuurmans, M Norouzi International conference on machine learning, 130-140, 2019 | 107 | 2019 |
| Compositional generalization via neural-symbolic stack machines X Chen, C Liang, AW Yu, D Song, D Zhou Advances in Neural Information Processing Systems 33, 1690-1701, 2020 | 95 | 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 | 32 | 2020 |
| Learning plausible inferences from semantic web knowledge by combining analogical generalization with structured logistic regression C Liang, K Forbus Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 22 | 2015 |
| Learning Paraphrase Identification with Structural Alignment C Liang, P Paritosh, V Rajendran, KD Forbus IJCAI 2016, 2016 | 21 | 2016 |
| Representation and computation in cognitive models KD Forbus, C Liang, I Rabkina Topics in cognitive science 9 (3), 694-718, 2017 | 20 | 2017 |
| Constructing hierarchical concepts via analogical generalization C Liang, K Forbus Proceedings of the Annual Meeting of the Cognitive Science Society 36 (36), 2014 | 11 | 2014 |
| Hyperscale hardware optimized neural architecture search S Li, G Andersen, T Chen, L Cheng, J Grady, D Huang, QV Le, A Li, X Li, ... Proceedings of the 28th ACM International Conference on Architectural …, 2023 | 6 | 2023 |
| Neural question answering system N Lao, C Liang, QV Le, J Blitzer US Patent App. 16/176,961, 2019 | 6 | 2019 |
| Unified functional hashing in automatic machine learning R Gillard, S Jonany, Y Miao, M Munn, C de Souza, J Dungay, C Liang, ... arXiv preprint arXiv:2302.05433, 2023 | 2 | 2023 |
Quoc V. LeResearch Scientist, GoogleVerified email at stanford.edu
