| Snorkel: Rapid training data creation with weak supervision A Ratner, SH Bach, H Ehrenberg, J Fries, S Wu, C Ré Proceedings of the VLDB endowment. International conference on very large …, 2017 | 1464 | 2017 |
| Data programming: Creating large training sets, quickly AJ Ratner, CM De Sa, S Wu, D Selsam, C Ré Advances in neural information processing systems 29, 2016 | 885 | 2016 |
| Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes CY Hsieh, CL Li, CK Yeh, H Nakhost, Y Fujii, A Ratner, R Krishna, CY Lee, ... arXiv preprint arXiv:2305.02301, 2023 | 461 | 2023 |
| Learning to compose domain-specific transformations for data augmentation AJ Ratner, H Ehrenberg, Z Hussain, J Dunnmon, C Ré Advances in neural information processing systems 30, 2017 | 430 | 2017 |
| Datacomp: In search of the next generation of multimodal datasets SY Gadre, G Ilharco, A Fang, J Hayase, G Smyrnis, T Nguyen, R Marten, ... Advances in Neural Information Processing Systems 36, 27092-27112, 2023 | 388 | 2023 |
| Deepdive: Declarative knowledge base construction C De Sa, A Ratner, C Ré, J Shin, F Wang, S Wu, C Zhang ACM SIGMOD Record 45 (1), 60-67, 2016 | 248* | 2016 |
| Training complex models with multi-task weak supervision A Ratner, B Hancock, J Dunnmon, F Sala, S Pandey, C Ré Proceedings of the AAAI conference on artificial intelligence 33 (01), 4763-4771, 2019 | 241 | 2019 |
| A kernel theory of modern data augmentation T Dao, A Gu, A Ratner, V Smith, C De Sa, C Ré International conference on machine learning, 1528-1537, 2019 | 239 | 2019 |
| Large language model as attributed training data generator: A tale of diversity and bias Y Yu, Y Zhuang, J Zhang, Y Meng, AJ Ratner, R Krishna, J Shen, C Zhang Advances in Neural Information Processing Systems 36, 55734-55784, 2023 | 203 | 2023 |
| Learning the structure of generative models without labeled data SH Bach, B He, A Ratner, C Ré International Conference on Machine Learning, 273-282, 2017 | 196 | 2017 |
| Snorkel drybell: A case study in deploying weak supervision at industrial scale SH Bach, D Rodriguez, Y Liu, C Luo, H Shao, C Xia, S Sen, A Ratner, ... Proceedings of the 2019 International Conference on Management of Data, 362-375, 2019 | 170 | 2019 |
| Swellshark: A generative model for biomedical named entity recognition without labeled data J Fries, S Wu, A Ratner, C Ré arXiv preprint arXiv:1704.06360, 2017 | 119 | 2017 |
| WRENCH: A comprehensive benchmark for weak supervision J Zhang, Y Yu, Y Li, Y Wang, Y Yang, M Yang, A Ratner arXiv preprint arXiv:2109.11377, 2021 | 116 | 2021 |
| AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature J Birgmeier, M Haeussler, CA Deisseroth, EH Steinberg, KA Jagadeesh, ... Science translational medicine 12 (544), eaau9113, 2020 | 108* | 2020 |
| A survey on programmatic weak supervision J Zhang, CY Hsieh, Y Yu, C Zhang, A Ratner arXiv preprint arXiv:2202.05433, 2022 | 89 | 2022 |
| Cross-modal data programming enables rapid medical machine learning JA Dunnmon, AJ Ratner, K Saab, N Khandwala, M Markert, H Sagreiya, ... Patterns 1 (2), 2020 | 84 | 2020 |
| Learning dependency structures for weak supervision models P Varma, F Sala, A He, A Ratner, C Ré International conference on machine learning, 6418-6427, 2019 | 77 | 2019 |
| Tool documentation enables zero-shot tool-usage with large language models CY Hsieh, SA Chen, CL Li, Y Fujii, A Ratner, CY Lee, R Krishna, T Pfister arXiv preprint arXiv:2308.00675, 2023 | 67 | 2023 |
| Snorkel metal: Weak supervision for multi-task learning A Ratner, B Hancock, J Dunnmon, R Goldman, C Ré Proceedings of the Second Workshop on Data Management for End-To-End Machine …, 2018 | 66 | 2018 |
| Weak supervision: the new programming paradigm for machine learning A Ratner, S Bach, P Varma, C Ré Hazy Research. Available via https://dawn. cs. stanford. edu//2017/07/16 …, 2019 | 64 | 2019 |
Christopher RéComputer Science, Stanford UniversityE-mail megerősítve itt: cs.stanford.edu
