| 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 | 520 | 2016 |
| Incremental knowledge base construction using deepdive J Shin, S Wu, F Wang, C De Sa, C Zhang, C Ré Proceedings of the VLDB Endowment International Conference on Very Large …, 2015 | 264 | 2015 |
| Representation tradeoffs for hyperbolic embeddings F Sala, C De Sa, A Gu, C Ré International conference on machine learning, 4460-4469, 2018 | 239 | 2018 |
| Improving neural network quantization without retraining using outlier channel splitting R Zhao, Y Hu, J Dotzel, C De Sa, Z Zhang International conference on machine learning, 7543-7552, 2019 | 174 | 2019 |
| Taming the wild: A unified analysis of hogwild-style algorithms CM De Sa, C Zhang, K Olukotun, C Ré Advances in neural information processing systems 28, 2015 | 173 | 2015 |
| Global convergence of stochastic gradient descent for some non-convex matrix problems C De Sa, C Re, K Olukotun International conference on machine learning, 2332-2341, 2015 | 172 | 2015 |
| Understanding and optimizing asynchronous low-precision stochastic gradient descent C De Sa, M Feldman, C Ré, K Olukotun Proceedings of the 44th annual international symposium on computer …, 2017 | 141 | 2017 |
| 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 | 109 | 2019 |
| Channel gating neural networks W Hua, Y Zhou, CM De Sa, Z Zhang, GE Suh Advances in Neural Information Processing Systems, 1884-1894, 2019 | 105 | 2019 |
| 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 | 99 | 2016 |
| High-accuracy low-precision training C De Sa, M Leszczynski, J Zhang, A Marzoev, CR Aberger, K Olukotun, ... arXiv preprint arXiv:1803.03383, 2018 | 92 | 2018 |
| Generating configurable hardware from parallel patterns R Prabhakar, D Koeplinger, KJ Brown, HJ Lee, C De Sa, C Kozyrakis, ... Acm Sigplan Notices 51 (4), 651-665, 2016 | 84 | 2016 |
| Parallel SGD: When does averaging help? J Zhang, C De Sa, I Mitliagkas, C Ré arXiv preprint arXiv:1606.07365, 2016 | 78 | 2016 |
| DeepDive: Declarative knowledge base construction C Zhang, C Ré, M Cafarella, C De Sa, A Ratner, J Shin, F Wang, S Wu Communications of the ACM 60 (5), 93-102, 2017 | 65 | 2017 |
| Have abstraction and eat performance, too: Optimized heterogeneous computing with parallel patterns KJ Brown, HJ Lee, T Romp, AK Sujeeth, C De Sa, C Aberger, K Olukotun 2016 IEEE/ACM International Symposium on Code Generation and Optimization …, 2016 | 61 | 2016 |
| Accelerated stochastic power iteration P Xu, B He, C De Sa, I Mitliagkas, C Re International Conference on Artificial Intelligence and Statistics, 58-67, 2018 | 57 | 2018 |
| SWALP: Stochastic weight averaging in low precision training G Yang, T Zhang, P Kirichenko, J Bai, AG Wilson, C De Sa International Conference on Machine Learning, 7015-7024, 2019 | 55 | 2019 |
| Ensuring rapid mixing and low bias for asynchronous Gibbs sampling C De Sa, C Re, K Olukotun International Conference on Machine Learning, 1567-1576, 2016 | 52 | 2016 |
| A formal framework for probabilistic unclean databases C De Sa, IF Ilyas, B Kimelfeld, C Ré, T Rekatsinas arXiv preprint arXiv:1801.06750, 2018 | 39 | 2018 |
| Gaussian quadrature for kernel features T Dao, CM De Sa, C Ré Advances in neural information processing systems 30, 2017 | 35 | 2017 |
