| 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 | 1360 | 2017 |
| Holistic Evaluation of Language Models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... arXiv preprint arXiv:2211.09110, 2022 | 1158 | 2022 |
| 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), 12474, 2016 | 1031 | 2016 |
| Asynchronous Decentralized Parallel Stochastic Gradient Descent X Lian, W Zhang, C Zhang, J Liu ICML, 2018 | 573 | 2018 |
| Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting D Cao, Y Wang, J Duan, C Zhang, X Zhu, C Huang, Y Tong, B Xu, J Bai, ... Advances in Neural Information Processing Systems 33, 2020 | 569 | 2020 |
| Towards Efficient Data Valuation Based on the Shapley Value R Jia, D Dao, B Wang, FA Hubis, M Gurel, N Hynes, B Li, C Zhang, ... AISTATS, 2019 | 491 | 2019 |
| D2: Decentralized Training over Decentralized Data H Tang, X Lian, M Yan, C Zhang, J Liu ICML, 2018 | 408 | 2018 |
| Advances, challenges and opportunities in creating data for trustworthy AI W Liang, GA Tadesse, D Ho, L Fei-Fei, M Zaharia, C Zhang, J Zou Nature Machine Intelligence 4 (8), 669-677, 2022 | 332 | 2022 |
| Communication compression for decentralized training H Tang, S Gan, C Zhang, T Zhang, J Liu Advances in Neural Information Processing Systems 31, 7652-7662, 2018 | 326 | 2018 |
| 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 | 312 | 2015 |
| 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 | 304 | 2017 |
| FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU Y Sheng, L Zheng, B Yuan, Z Li, M Ryabinin, B Chen, P Liang, C Re, ... ICML, 2023 | 300 | 2023 |
| Doublesqueeze: Parallel stochastic gradient descent with double-pass error-compensated compression H Tang, C Yu, X Lian, T Zhang, J Liu International Conference on Machine Learning, 6155-6165, 2019 | 271 | 2019 |
| DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference. F Niu, C Zhang, C Ré, JW Shavlik VLDS 12, 25-28, 2012 | 268 | 2012 |
| 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 | 253* | 2017 |
| Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms R Jia, D Dao, B Wang, FA Hubis, NM Gurel, B Li, C Zhang, CJ Spanos, ... VLDB, 2019 | 243 | 2019 |
| Heterogeneity-Aware Distributed Parameter Servers J Jiang, B Cui, C Zhang, LYHAD Parameter 2017 ACM International Conference on Management of Data (SIGMOD) 10 (3035918 …, 2017 | 232 | 2017 |
| Deja vu: Contextual sparsity for efficient llms at inference time Z Liu, W Jue, T Dao, T Zhou, B Yuan, Z Song, A Shrivastava, C Zhang, ... ICML, 2023 | 222 | 2023 |
| A principled approach to data valuation for federated learning T Wang, J Rausch, C Zhang, R Jia, D Song Federated Learning: Privacy and Incentive, 153-167, 2020 | 218 | 2020 |
| 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, 2674-2682, 2015 | 217 | 2015 |
Christopher RéComputer Science, Stanford UniversityE-mail megerősítve itt: cs.stanford.edu
