Ming Lin

According to our database, Ming Lin authored at least 43 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunities.

Guihong Li, Duc Hoang, Kartikeya Bhardwaj, Ming Lin, Zhangyang Wang, Radu Marculescu

IEEE Trans. Pattern Anal. Mach. Intell., December, 2024

2023

Learning the Relation Between Similarity Loss and Clustering Loss in Self-Supervised Learning.

Jidong Ge, Yuxiang Liu, Jie Gui, Lanting Fang, Ming Lin, James Tin-Yau Kwok, Liguo Huang, Bin Luo

IEEE Trans. Image Process., 2023

DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network.

Xuan Shen, Yaohua Wang, Ming Lin, Yilun Huang, Hao Tang, Xiuyu Sun, Yanzhi Wang

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Making Vision Transformers Efficient from A Token Sparsification View.

Shuning Chang, Pichao Wang, Ming Lin, Fan Wang, David Junhao Zhang, Rong Jin, Mike Zheng Shou

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Robust Graph Structure Learning over Images via Multiple Statistical Tests.

Yaohua Wang, Fangyi Zhang, Ming Lin, Senzhang Wang, Xiuyu Sun, Rong Jin

CoRR, 2022

Robust Graph Structure Learning via Multiple Statistical Tests.

Yaohua Wang, Fangyi Zhang, Ming Lin, Senzhang Wang, Xiuyu Sun, Rong Jin

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection.

Zhenhong Sun, Ming Lin, Xiuyu Sun, Zhiyu Tan, Hao Li, Rong Jin

Proceedings of the International Conference on Machine Learning, 2022

Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space.

Yaohua Wang, Yaobin Zhang, Fangyi Zhang, Senzhang Wang, Ming Lin, YuQi Zhang, Xiuyu Sun

Proceedings of the Tenth International Conference on Learning Representations, 2022

Entroformer: A Transformer-based Entropy Model for Learned Image Compression.

Yichen Qian, Xiuyu Sun, Ming Lin, Zhiyu Tan, Rong Jin

Proceedings of the Tenth International Conference on Learning Representations, 2022

KVT: k-NN Attention for Boosting Vision Transformers.

Pichao Wang, Xue Wang, Fan Wang, Ming Lin, Shuning Chang, Hao Li, Rong Jin

Proceedings of the Computer Vision, 2022

2021

Revisiting Efficient Object Detection Backbones from Zero-Shot Neural Architecture Search.

Zhenhong Sun, Ming Lin, Xiuyu Sun, Zhiyu Tan, Rong Jin

CoRR, 2021

Fine-Grained AutoAugmentation for Multi-Label Classification.

Ya Wang, Hesen Chen, Fangyi Zhang, Yaohua Wang, Xiuyu Sun, Ming Lin, Hao Li

CoRR, 2021

KVT: k-NN Attention for Boosting Vision Transformers.

Pichao Wang, Xue Wang, Fan Wang, Ming Lin, Shuning Chang, Wen Xie, Hao Li, Rong Jin

CoRR, 2021

Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition.

Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin

CoRR, 2021

Learning Accurate Entropy Model with Global Reference for Image Compression.

Yichen Qian, Zhiyu Tan, Xiuyu Sun, Ming Lin, Dongyang Li, Zhenhong Sun, Hao Li, Rong Jin

Proceedings of the 9th International Conference on Learning Representations, 2021

Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition.

Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

WeMix: How to Better Utilize Data Augmentation.

Yi Xu, Asaf Noy, Ming Lin, Qi Qian, Hao Li, Rong Jin

CoRR, 2020

Neural Architecture Design for GPU-Efficient Networks.

Ming Lin, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin

CoRR, 2020

2019

Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement.

Ming Lin, Xiaomin Song, Qi Qian, Hao Li, Liang Sun, Shenghuo Zhu, Rong Jin

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

MuffNet: Multi-Layer Feature Federation for Mobile Deep Learning.

Hesen Chen, Ming Lin, Xiuyu Sun, Qian Qi, Hao Li, Rong Jin

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019

Which Factorization Machine Modeling Is Better: A Theoretical Answer with Optimal Guarantee.

Ming Lin, Shuang Qiu, Jieping Ye, Xiaomin Song, Qi Qian, Liang Sun, Shenghuo Zhu, Rong Jin

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

On the Generalization Ability of Online Gradient Descent Algorithm Under the Quadratic Growth Condition.

Daqing Chang, Ming Lin, Changshui Zhang

IEEE Trans. Neural Networks Learn. Syst., 2018

Robust finite mixture regression for heterogeneous targets.

Jian Liang, Kun Chen, Ming Lin, Changshui Zhang, Fei Wang

Data Min. Knowl. Discov., 2018

2017

Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection.

Xiaojun Chang, Zhigang Ma, Ming Lin, Yi Yang, Alexander G. Hauptmann

IEEE Trans. Image Process., 2017

Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis.

Xiang Li, Aoxiao Zhong, Ming Lin, Ning Guo, Mu Sun, Arkadiusz Sitek, Jieping Ye, James Thrall, Quanzheng Li

Proceedings of the Machine Learning in Medical Imaging - 8th International Workshop, 2017

2016

Online kernel learning with nearly constant support vectors.

Ming Lin, Lijun Zhang, Rong Jin, Shifeng Weng, Changshui Zhang

Neurocomputing, 2016

Strategies for Searching Video Content with Text Queries or Video Examples.

Shoou-I Yu, Yi Yang, Zhongwen Xu, Shicheng Xu, Deyu Meng, Zexi Mao, Zhigang Ma, Ming Lin, Xuanchong Li, Huan Li, Zhen-Zhong Lan, Lu Jiang, Alexander G. Hauptmann, Chuang Gan, Xingzhong Du, Xiaojun Chang

CoRR, 2016

The Best of BothWorlds: Combining Data-Independent and Data-Driven Approaches for Action Recognition.

Zhen-Zhong Lan, Shoou-I Yu, Dezhong Yao, Ming Lin, Bhiksha Raj, Alexander G. Hauptmann

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2016

2015

Dependent Online Kernel Learning With Constant Number of Random Fourier Features.

Zhen Hu, Ming Lin, Changshui Zhang

IEEE Trans. Neural Networks Learn. Syst., 2015

Large-scale eigenvector approximation via Hilbert Space Embedding Nyström.

Ming Lin, Fei Wang, Changshui Zhang

Pattern Recognit., 2015

Damping proximal coordinate descent algorithm for non-convex regularization.

Zheng Pan, Ming Lin, Guangdong Hou, Changshui Zhang

Neurocomputing, 2015

The Best of Both Worlds: Combining Data-independent and Data-driven Approaches for Action Recognition.

Zhen-Zhong Lan, Dezhong Yao, Ming Lin, Shoou-I Yu, Alexander G. Hauptmann

CoRR, 2015

Handcrafted Local Features are Convolutional Neural Networks.

Zhen-Zhong Lan, Shoou-I Yu, Ming Lin, Bhiksha Raj, Alexander G. Hauptmann

CoRR, 2015

Long-short Term Motion Feature for Action Classification and Retrieval.

Zhen-Zhong Lan, Xuanchong Li, Ming Lin, Alexander G. Hauptmann

CoRR, 2015

CMU Informedia@TRECVID 2015: MED/SIN/LNK/SED.

Shoou-I Yu, Lu Jiang, Zhongwen Xu, Zhenzhong Lan, Shicheng Xu, Xiaojun Chang, Xuanchong Li, Zexi Mao, Chuang Gan, Yajie Miao, Xingzhong Du, Yang Cai, Lara J. Martin, Nikolas Wolfe, Anurag Kumar, Huan Li, Ming Lin, Zhigang Ma, Yi Yang, Deyu Meng, Shiguang Shan, Pinar Duygulu Sahin, Susanne Burger, Florian Metze, Rita Singh, Bhiksha Raj, Teruko Mitamura, Richard M. Stern, Alexander G. Hauptmann

Proceedings of the 2015 TREC Video Retrieval Evaluation, 2015

Density Corrected Sparse Recovery when R.I.P. Condition Is Broken.

Ming Lin, Zhen-Zhong Lan, Alexander G. Hauptmann

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Beyond Gaussian Pyramid: Multi-skip Feature Stacking for action recognition.

Zhen-Zhong Lan, Ming Lin, Xuanchong Li, Alexander G. Hauptmann, Bhiksha Raj

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

Exploring Semantic Inter-Class Relationships (SIR) for Zero-Shot Action Recognition.

Chuang Gan, Ming Lin, Yi Yang, Yueting Zhuang, Alexander G. Hauptmann

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Efficient Sparse Recovery via Adaptive Non-Convex Regularizers with Oracle Property.

Ming Lin, Rong Jin, Changshui Zhang

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Informedia @ TRECVID 2014.

Shoou-I Yu, Lu Jiang, Zhongwen Xu, Zhenzhong Lan, Shicheng Xu, Xiaojun Chang, Xuanchong Li, Zexi Mao, Chuang Gan, Yajie Miao, Xingzhong Du, Yang Cai, Lara J. Martin, Nikolas Wolfe, Anurag Kumar, Huan Li, Ming Lin, Zhigang Ma, Yi Yang, Deyu Meng, Shiguang Shan, Pinar Duygulu Sahin, Susanne Burger, Florian Metze, Rita Singh, Bhiksha Raj, Teruko Mitamura, Richard M. Stern, Alexander G. Hauptmann, Anil Armagan, Yicheng Zhao

Proceedings of the 2014 TREC Video Retrieval Evaluation, 2014

2013

On the Sample Complexity of Random Fourier Features for Online Learning: How Many Random Fourier Features Do We Need?

Ming Lin, Shifeng Weng, Changshui Zhang

ACM Trans. Knowl. Discov. Data, 2013

Online Kernel Learning with a Near Optimal Sparsity Bound.

Lijun Zhang, Jinfeng Yi, Rong Jin, Ming Lin, Xiaofei He

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

A general framework for transfer sparse subspace learning.

Shizhun Yang, Ming Lin, Chenping Hou, Changshui Zhang, Yi Wu

Neural Comput. Appl., 2012