Wei Zhang
Wei Zhang
IBM T.J.Watson Research Center
Verified email at - Homepage
Cited by
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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
Asynchronous decentralized parallel stochastic gradient descent
X Lian*, W Zhang*, C Zhang, J Liu
ICML 2018, 2017
Staleness-aware Async-SGD for Distributed Deep Learning
W Zhang, S Gupta, X Lian, J Liu
IJCAI 2016, 2016
Automated atomicity-violation fixing
G Jin, L Song, W Zhang, S Lu, B Liblit
PLDI 2011, 389-400, 2011
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, 2021
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
Automated concurrency-bug fixing
G Jin, W Zhang, D Deng, B Liblit, S Lu
OSDI 2012, 221-236, 2012
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
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
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
ConMem: detecting severe concurrency bugs through an effect-oriented approach
W Zhang, C Sun, S Lu
ASPLOS'10 45 (3), 179-192, 2010
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks
X Cui, Z Wei, Z Tuske, M Picheny
NIPS'2018, 2018
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
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
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
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
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
Forecast of Solar Energy Production - A Deep Learning Approach
R Zhang, F Minwei, Z Wei, L Siyuan, W Fei
Quality-directed adaptive analytic retraining
DP Grove, MJ Hirzel, W Zhang
US Patent 10,163,061, 2018
Efficient Concurrency-Bug Detection Across Inputs
D Deng, W Zhang, L Shan
Object-Oriented Programming, Systems, Languages & Applications, 2013
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