Jinmian Ye

According to our database, Jinmian Ye authored at least 11 papers between 2017 and 2020.

Collaborative distances:

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

2020

Block-term tensor neural networks.

Jinmian Ye, Guangxi Li, Di Chen, Haiqin Yang, Shandian Zhe, Zenglin Xu

Neural Networks, 2020

2019

Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks.

Zhonghui You, Kun Yan, Jinmian Ye, Meng Ma, Ping Wang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Adversarial Noise Layer: Regularize Neural Network by Adding Noise.

Zhonghui You, Jinmian Ye, Kunming Li, Zenglin Xu, Ping Wang

Proceedings of the 2019 IEEE International Conference on Image Processing, 2019

Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition.

Yu Pan, Jing Xu, Maolin Wang, Jinmian Ye, Fei Wang, Kun Bai, Zenglin Xu

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

2018

TensorD: A tensor decomposition library in TensorFlow.

Liyang Hao, Siqi Liang, Jinmian Ye, Zenglin Xu

Neurocomputing, 2018

Adversarial Noise Layer: Regularize Neural Network By Adding Noise.

Zhonghui You, Jinmian Ye, Kunming Li, Ping Wang

CoRR, 2018

Superneurons: dynamic GPU memory management for training deep neural networks.

Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, Tim Kraska

Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2018

Learning Compact Recurrent Neural Networks With Block-Term Tensor Decomposition.

Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition.

Guangxi Li, Jinmian Ye, Haiqin Yang, Di Chen, Shuicheng Yan, Zenglin Xu

CoRR, 2017

Efficient Communications in Training Large Scale Neural Networks.

Yiyang Zhao, Linnan Wang, Wei Wu, George Bosilca, Richard W. Vuduc, Jinmian Ye, Wenqi Tang, Zenglin Xu

Proceedings of the on Thematic Workshops of ACM Multimedia 2017, Mountain View, CA, USA, October 23, 2017

Simple and efficient parallelization for probabilistic temporal tensor factorization.

Guangxi Li, Zenglin Xu, Linnan Wang, Jinmian Ye, Irwin King, Michael R. Lyu

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017