| 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 Advances in neural information processing systems 30, 2017 | 907 | 2017 |
| Asynchronous decentralized parallel stochastic gradient descent X Lian*, W Zhang*, C Zhang, J Liu ICML 2018, 2017 | 398 | 2017 |
| Automated atomicity-violation fixing G Jin, L Song, W Zhang, S Lu, B Liblit PLDI 2011, 389-400, 2011 | 290 | 2011 |
| Staleness-aware Async-SGD for Distributed Deep Learning W Zhang, S Gupta, X Lian, J Liu IJCAI 2016, 2016 | 279 | 2016 |
| Automated concurrency-bug fixing G Jin, W Zhang, D Deng, B Liblit, S Lu OSDI 2012, 221-236, 2012 | 215 | 2012 |
| Model accuracy and runtime tradeoff in distributed deep learning: A systematic study S Gupta*, W Zhang*, F Wang 2016 IEEE 16th International Conference on Data Mining (ICDM), 171-180, 2016 | 200* | 2016 |
| ConSeq: detecting concurrency bugs through sequential errors W Zhang, J Lim, R Olichandran, J Scherpelz, G Jin, S Lu, T Reps ASPLOS 2011, 251-264, 2011 | 167 | 2011 |
| Adacomp: Adaptive residual gradient compression for data-parallel distributed training CY Chen, J Choi, D Brand, A Agrawal, W Zhang, K Gopalakrishnan Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 155 | 2018 |
| ConMem: detecting severe concurrency bugs through an effect-oriented approach W Zhang, C Sun, S Lu ASPLOS'10 45 (3), 179-192, 2010 | 155 | 2010 |
| Hybrid 8-bit floating point (HFP8) training and inference for deep neural networks X Sun, J Choi, CY Chen, N Wang, S Venkataramani, VV Srinivasan, X Cui, ... Advances in neural information processing systems 32, 2019 | 145 | 2019 |
| Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks X Cui, Z Wei, Z Tuske, M Picheny NIPS'2018, 2018 | 86 | 2018 |
| GLB: lifeline-based global load balancing library in x10 W Zhang, O Tardieu, D Grove, B Herta, T Kamada, V Saraswat, ... Proceedings of the first workshop on Parallel programming for analytics …, 2014 | 66 | 2014 |
| Towards better understanding of adaptive gradient algorithms in generative adversarial nets M Liu, Y Mroueh, J Ross, W Zhang, X Cui, P Das, T Yang arXiv preprint arXiv:1912.11940, 2019 | 54 | 2019 |
| ConAir: Featherweight concurrency bug recovery via single-threaded idempotent execution W Zhang, M De Kruijf, A Li, S Lu, K Sankaralingam Proceedings of the eighteenth international conference on Architectural …, 2013 | 52 | 2013 |
| Project codenet: A large-scale ai for code dataset for learning a diversity of coding tasks R Puri, DS Kung, G Janssen, W Zhang, G Domeniconi, V Zolotov, J Dolby, ... arXiv preprint arXiv:2105.12655 1035, 2021 | 49 | 2021 |
| Decentralized Parallel Algorithm for Training Generative Adversarial Nets ML Liu, W Zhang, Y Mroueh, X Cui, J Ross, P Das Conference on Neural Information Processing Systems, 2020 | 46 | 2020 |
| Scalecom: Scalable sparsified gradient compression for communication-efficient distributed training CY Chen, J Ni, S Lu, X Cui, PY Chen, X Sun, N Wang, S Venkataramani, ... Advances in Neural Information Processing Systems 33, 13551-13563, 2020 | 42 | 2020 |
| Efficient Concurrency-Bug Detection Across Inputs D Deng, W Zhang, L Shan Object-Oriented Programming, Systems, Languages & Applications, 2013 | 42 | 2013 |
| Quality-directed adaptive analytic retraining DP Grove, MJ Hirzel, W Zhang US Patent 10,163,061, 2018 | 37 | 2018 |
| Forecast of Solar Energy Production - A Deep Learning Approach R Zhang, F Minwei, Z Wei, L Siyuan, W Fei | 34* | |
Xiaodong CuiPrincipal Research Scientist, IBM T. J. Watson Research CenterVerified email at us.ibm.com
