Obserwuj
Minjie Wang
Minjie Wang
Zweryfikowany adres z nyu.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
T Chen, M Li, Y Li, M Lin, N Wang, M Wang, T Xiao, B Xu, C Zhang, ...
arXiv preprint arXiv:1512.01274, 2015
27172015
Deep graph library: A graph-centric, highly-performant package for graph neural networks
M Wang, D Zheng, Z Ye, Q Gan, M Li, X Song, J Zhou, C Ma, L Yu, Y Gai, ...
arXiv preprint arXiv:1909.01315, 2019
11392019
Deep graph library: Towards efficient and scalable deep learning on graphs
MY Wang
ICLR workshop on representation learning on graphs and manifolds, 2019
6982019
Supporting very large models using automatic dataflow graph partitioning
M Wang, C Huang, J Li
Proceedings of the Fourteenth EuroSys Conference 2019, 1-17, 2019
1382019
Distdgl: distributed graph neural network training for billion-scale graphs
D Zheng, C Ma, M Wang, J Zhou, Q Su, X Song, Q Gan, Z Zhang, ...
2020 IEEE/ACM 10th Workshop on Irregular Applications: Architectures and …, 2020
123*2020
Qidong Su, Xiang Song, Quan Gan, Zheng Zhang, and George Karypis. 2020. DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
CM Da Zheng, M Wang, J Zhou
arXiv preprint arXiv:2010.05337, 2020
982020
Featgraph: A flexible and efficient backend for graph neural network systems
Y Hu, Z Ye, M Wang, J Yu, D Zheng, M Li, Z Zhang, Z Zhang, Y Wang
SC20: International Conference for High Performance Computing, Networking …, 2020
772020
Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv 2015
T Chen, M Li, Y Li, M Lin, N Wang, M Wang, T Xiao, B Xu, C Zhang, ...
arXiv preprint arXiv:1512.01274, 0
72
A calculational model of shear strain and strain rate within shear band in a serrated chip formed during high speed machining
CZ Duan, MJ Wang, JZ Pang, GH Li
Journal of materials processing technology 178 (1-3), 274-277, 2006
542006
Minerva: A scalable and highly efficient training platform for deep learning
M Wang, T Xiao, J Li, J Zhang, C Hong, Z Zhang
NIPS Workshop, Distributed Machine Learning and Matrix Computations, 51, 2014
322014
Deep graph library: towards efficient and scalable deep learning on graphs. CoRR abs/1909.01315 (2019)
M Wang, L Yu, QG Da Zheng, Y Gai, Z Ye, M Li, J Zhou, Q Huang, C Ma, ...
arXiv preprint arXiv:1909.01315, 2019
292019
Study on design and experiments of extrusion die for polypropylene single-lumen micro tubes
GB Jin, DY Zhao, MJ Wang, YF Jin, HQ Tian, J Zhang
Microsystem Technologies 21, 2495-2503, 2015
262015
Unifying data, model and hybrid parallelism in deep learning via tensor tiling
M Wang, C Huang, J Li
arXiv preprint arXiv:1805.04170, 2018
252018
Prediction of cutting force in five-axis flat-end milling
ZC Wei, ML Guo, MJ Wang, SQ Li, SX Liu
The International Journal of Advanced Manufacturing Technology 96, 137-152, 2018
252018
Impression store: Compressive sensing-based storage for big data analytics
J Zhang, Y Yan, LJ Chen, M Wang, T Moscibroda, Z Zhang
6th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 14), 2014
252014
Distributed hybrid cpu and gpu training for graph neural networks on billion-scale heterogeneous graphs
D Zheng, X Song, C Yang, D LaSalle, G Karypis
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022
222022
Scalable graph neural networks with deep graph library
D Zheng, M Wang, Q Gan, X Song, Z Zhang, G Karypis
Proceedings of the 14th ACM International Conference on Web Search and Data …, 2021
212021
Graphiler: Optimizing graph neural networks with message passing data flow graph
Z Xie, M Wang, Z Ye, Z Zhang, R Fan
Proceedings of Machine Learning and Systems 4, 515-528, 2022
182022
Learning graph neural networks with deep graph library
D Zheng, M Wang, Q Gan, Z Zhang, G Karypis
Companion Proceedings of the Web Conference 2020, 305-306, 2020
162020
Slicing parameters optimizing and experiments based on constant wire wear loss model in multi-wire saw
Z Li, MJ Wang, X Pan, YM Ni
The International Journal of Advanced Manufacturing Technology 81, 329-334, 2015
162015
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