| 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 | 541* | 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 | 409 | 2016 |
| Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent X Lian, C Zhang, H Zhang, CJ Hsieh, W Zhang, J Liu NIPS, 2017 | 255 | 2017 |
| Materialization optimizations for feature selection workloads C Zhang, A Kumar, C Ré ACM Transactions on Database Systems (TODS) 41 (1), 2, 2016 | 131 | 2016 |
| 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, 2674-2682, 2015 | 127 | 2015 |
| Dimmwitted: A study of main-memory statistical analytics C Zhang, C Ré Proceedings of the VLDB Endowment 7 (12), 1283-1294, 2014 | 125 | 2014 |
| The use of categorization information in language models for question retrieval X Cao, G Cong, B Cui, CS Jensen, C Zhang Proceedings of the 18th ACM conference on Information and knowledge …, 2009 | 122 | 2009 |
| Brainwash: A Data System for Feature Engineering. MR Anderson, D Antenucci, V Bittorf, M Burgess, MJ Cafarella, A Kumar, ... CIDR, 2013 | 121 | 2013 |
| Asynchronous Decentralized Parallel Stochastic Gradient Descent X Lian, W Zhang, C Zhang, J Liu ICML, 2018 | 114 | 2018 |
| Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit K Schawinski, C Zhang, H Zhang, L Fowler, GK Santhanam Monthly Notices of the Royal Astronomical Society: Letters 467 (1), L110-L114, 2017 | 114 | 2017 |
| ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning H Zhang, J Li, K Kara, D Alistarh, J Liu, C Zhang International Conference on Machine Learning, 4035-4043, 2017 | 111* | 2017 |
| Elementary: Large-scale knowledge-base construction via machine learning and statistical inference F Niu, C Zhang, C Ré, J Shavlik International Journal on Semantic Web and Information Systems (IJSWIS) 8 (3 …, 2012 | 100 | 2012 |
| Building an entity-centric stream filtering test collection for TREC 2012 JR Frank, M Kleiman-Weiner, DA Roberts, F Niu, C Zhang, C Ré, ... MASSACHUSETTS INST OF TECH CAMBRIDGE, 2012 | 96 | 2012 |
| Asynchrony begets momentum, with an application to deep learning I Mitliagkas, C Zhang, S Hadjis, C Ré 2016 54th Annual Allerton Conference on Communication, Control, and …, 2016 | 92 | 2016 |
| Heterogeneity-aware distributed parameter servers J Jiang, B Cui, C Zhang, L Yu Proceedings of the 2017 ACM International Conference on Management of Data …, 2017 | 83 | 2017 |
| D2: Decentralized Training over Decentralized Data H Tang, X Lian, M Yan, C Zhang, J Liu ICML, 2018 | 78 | 2018 |
| Large-scale extraction of gene interactions from full-text literature using DeepDive EK Mallory, C Zhang, C Ré, RB Altman Bioinformatics 32 (1), 106-113, 2016 | 74 | 2016 |
| A machine reading system for assembling synthetic paleontological databases SE Peters, C Zhang, M Livny, C Ré PLoS One 9 (12), e113523, 2014 | 73 | 2014 |
| Communication compression for decentralized training H Tang, S Gan, C Zhang, T Zhang, J Liu Advances in Neural Information Processing Systems, 7652-7662, 2018 | 71* | 2018 |
| Towards high-throughput Gibbs sampling at scale: A study across storage managers C Zhang, C Ré Proceedings of the 2013 ACM SIGMOD International Conference on Management of …, 2013 | 57 | 2013 |
Christopher RéComputer Science, Stanford UniversityVerified email at cs.stanford.edu
