Mu Li
Mu Li
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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
Empirical evaluation of rectified activations in convolutional network
B Xu, N Wang, T Chen, M Li
arXiv preprint arXiv:1505.00853, 2015
Scaling distributed machine learning with the parameter server
M Li, DG Andersen, JW Park, AJ Smola, A Ahmed, V Josifovski, J Long, ...
11th {USENIX} Symposium on Operating Systems Design and Implementation …, 2014
Efficient mini-batch training for stochastic optimization
M Li, T Zhang, Y Chen, AJ Smola
Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014
Communication efficient distributed machine learning with the parameter server
M Li, DG Andersen, AJ Smola, K Yu
Advances in Neural Information Processing Systems, 19-27, 2014
Emotion classification based on gamma-band EEG
M Li, BL Lu
2009 Annual International Conference of the IEEE Engineering in medicine and …, 2009
Bag of tricks for image classification with convolutional neural networks
T He, Z Zhang, H Zhang, Z Zhang, J Xie, M Li
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
Parameter Server for Distributed Machine Learning
M Li, L Zhou, Z Yang, A Li, F Xia, DG Andersen, A Smola
Making large-scale Nyström approximation possible
M Li, JTY Kwok, B Lü
ICML 2010-Proceedings, 27th International Conference on Machine Learning, 631, 2010
Dive into deep learning
A Zhang, ZC Lipton, M Li, AJ Smola
Unpublished Draft. Retrieved 19, 2019, 2019
Large-scale Nyström kernel matrix approximation using randomized SVD
M Li, W Bi, JT Kwok, BL Lu
IEEE transactions on neural networks and learning systems 26 (1), 152-164, 2014
Iterative row sampling
M Li, GL Miller, R Peng
2013 IEEE 54th Annual Symposium on Foundations of Computer Science, 127-136, 2013
Time and space efficient spectral clustering via column sampling
M Li, XC Lian, JT Kwok, BL Lu
CVPR 2011, 2297-2304, 2011
xgboost: extreme gradient boosting. R package version 0.71. 2
T Chen, T He, M Benesty, V Khotilovich, Y Tang, H Cho, K Chen, ...
Resnest: Split-attention networks
H Zhang, C Wu, Z Zhang, Y Zhu, Z Zhang, H Lin, Y Sun, T He, J Mueller, ...
arXiv preprint arXiv:2004.08955, 2020
Difacto: Distributed factorization machines
M Li, Z Liu, AJ Smola, YX Wang
Proceedings of the Ninth ACM International Conference on Web Search and Data …, 2016
Distributed delayed proximal gradient methods
M Li, DG Andersen, A Smola
NIPS Workshop on Optimization for Machine Learning 3, 3, 2013
Inferring movement trajectories from GPS snippets
M Li, A Ahmed, AJ Smola
Proceedings of the Eighth ACM International Conference on Web Search and …, 2015
Revise saturated activation functions
B Xu, R Huang, M Li
arXiv preprint arXiv:1602.05980, 2016
xgboost: Extreme Gradient Boosting (2017)
T Chen, T He, M Benesty, V Khotilovich, Y Tang, H Cho, K Chen, ...
R package version 0.6-4, 2015
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