| Hogwild: A lock-free approach to parallelizing stochastic gradient descent F Niu, B Recht, C Ré, S Wright Advances in Neural Information Processing Systems, 693-701, 2011 | 2371* | 2011 |
| 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 | 819 | 2017 |
| Incremental knowledge base construction using DeepDive C De Sa, A Ratner, C Ré, J Shin, F Wang, S Wu, C Zhang The VLDB Journal 26 (1), 81-105, 2017 | 727* | 2017 |
| Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features KH Yu, C Zhang, GJ Berry, RB Altman, C Ré, DL Rubin, M Snyder Nature communications 7 (1), 1-10, 2016 | 711 | 2016 |
| Probabilistic databases D Suciu, D Olteanu, C Ré, C Koch Synthesis lectures on data management 3 (2), 1-180, 2011 | 573 | 2011 |
| 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 | 532 | 2016 |
| Efficient top-k query evaluation on probabilistic data C Re, N Dalvi, D Suciu 2007 IEEE 23rd International Conference on Data Engineering, 886-895, 2007 | 465 | 2007 |
| The MADlib analytics library or MAD skills, the SQL J Hellerstein, C Ré, F Schoppmann, DZ Wang, E Fratkin, A Gorajek, ... arXiv preprint arXiv:1208.4165, 2012 | 461 | 2012 |
| An asynchronous parallel stochastic coordinate descent algorithm J Liu, S Wright, C Ré, V Bittorf, S Sridhar International Conference on Machine Learning, 469-477, 2014 | 385 | 2014 |
| Parallel stochastic gradient algorithms for large-scale matrix completion B Recht, C Ré Mathematical Programming Computation 5 (2), 201-226, 2013 | 362 | 2013 |
| Tuffy: Scaling up statistical inference in markov logic networks using an rdbms F Niu, C Ré, AH Doan, J Shavlik arXiv preprint arXiv:1104.3216, 2011 | 345 | 2011 |
| Holoclean: Holistic data repairs with probabilistic inference T Rekatsinas, X Chu, IF Ilyas, C Ré arXiv preprint arXiv:1702.00820, 2017 | 332 | 2017 |
| Hyperbolic graph convolutional neural networks I Chami, Z Ying, C Ré, J Leskovec Advances in neural information processing systems 32, 2019 | 297 | 2019 |
| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 294 | 2021 |
| Worst-case optimal join algorithms HQ Ngo, E Porat, C Ré, A Rudra Journal of the ACM (JACM) 65 (3), 1-40, 2018 | 276 | 2018 |
| 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 | 268 | 2017 |
| Dawnbench: An end-to-end deep learning benchmark and competition C Coleman, D Narayanan, D Kang, T Zhao, J Zhang, L Nardi, P Bailis, ... Training 100 (101), 102, 2017 | 264 | 2017 |
| Event queries on correlated probabilistic streams C Ré, J Letchner, M Balazinksa, D Suciu Proceedings of the 2008 ACM SIGMOD international conference on Management of …, 2008 | 264 | 2008 |
| Factoring nonnegative matrices with linear programs B Recht, C Re, J Tropp, V Bittorf Advances in neural information processing systems 25, 2012 | 256 | 2012 |
| MYSTIQ: a system for finding more answers by using probabilities J Boulos, N Dalvi, B Mandhani, S Mathur, C Re, D Suciu Proceedings of the 2005 ACM SIGMOD international conference on Management of …, 2005 | 255 | 2005 |
Ce ZhangETH Zurichinf.ethz.ch의 이메일 확인됨
