| Empirical evaluation of rectified activations in convolutional network B Xu, N Wang, T Chen, M Li arXiv preprint arXiv:1505.00853, 2015 | 3280 | 2015 |
| 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 | 2504 | 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 | 1890 | 2014 |
| 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/CVF conference on computer vision and pattern …, 2019 | 1235 | 2019 |
| Resnest: Split-attention networks H Zhang, C Wu, Z Zhang, Y Zhu, H Lin, Z Zhang, Y Sun, T He, J Mueller, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 1158 | 2022 |
| 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 | 830 | 2014 |
| Dive into deep learning A Zhang, ZC Lipton, M Li, AJ Smola arXiv preprint arXiv:2106.11342, 2021 | 813 | 2021 |
| Communication efficient distributed machine learning with the parameter server M Li, DG Andersen, AJ Smola, K Yu Advances in Neural Information Processing Systems 27, 2014 | 599 | 2014 |
| Emotion classification based on gamma-band EEG M Li, BL Lu 2009 Annual International Conference of the IEEE Engineering in medicine and …, 2009 | 470 | 2009 |
| Autogluon-tabular: Robust and accurate automl for structured data N Erickson, J Mueller, A Shirkov, H Zhang, P Larroy, M Li, A Smola arXiv preprint arXiv:2003.06505, 2020 | 294 | 2020 |
| Parameter Server for Distributed Machine Learning M Li, L Zhou, Z Yang, A Li, F Xia, DG Andersen, A Smola | 233 | 2013 |
| Gluoncv and gluonnlp: Deep learning in computer vision and natural language processing J Guo, H He, T He, L Lausen, M Li, H Lin, X Shi, C Wang, J Xie, S Zha, ... The Journal of Machine Learning Research 21 (1), 845-851, 2020 | 175 | 2020 |
| Bag of freebies for training object detection neural networks Z Zhang, T He, H Zhang, Z Zhang, J Xie, M Li arXiv preprint arXiv:1902.04103, 2019 | 174 | 2019 |
| Making large-scale Nyström approximation possible M Li, JTY Kwok, B Lü Proceedings of the 27th International Conference on Machine Learning, ICML …, 2010 | 151 | 2010 |
| 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 | 124 | 2014 |
| Optimizing CNN model inference on CPUs Y Liu, Y Wang, R Yu, M Li, V Sharma, Y Wang | 121 | 2019 |
| Iterative row sampling M Li, GL Miller, R Peng 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, 127-136, 2013 | 111 | 2013 |
| A comprehensive study of deep video action recognition Y Zhu, X Li, C Liu, M Zolfaghari, Y Xiong, C Wu, Z Zhang, J Tighe, ... arXiv preprint arXiv:2012.06567, 2020 | 101 | 2020 |
| Language models with transformers C Wang, M Li, AJ Smola arXiv preprint arXiv:1904.09408, 2019 | 99 | 2019 |
| 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 | 83 | 2015 |
