Jinmian Ye

762 total citations
7 papers, 160 citations indexed

About

Jinmian Ye is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mathematics. According to data from OpenAlex, Jinmian Ye has authored 7 papers receiving a total of 160 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 3 papers in Computational Mathematics. Recurrent topics in Jinmian Ye's work include Advanced Neural Network Applications (4 papers), Tensor decomposition and applications (3 papers) and Parallel Computing and Optimization Techniques (2 papers). Jinmian Ye is often cited by papers focused on Advanced Neural Network Applications (4 papers), Tensor decomposition and applications (3 papers) and Parallel Computing and Optimization Techniques (2 papers). Jinmian Ye collaborates with scholars based in China, United States and Australia. Jinmian Ye's co-authors include Zenglin Xu, Ping Wang, Kun Yan, Meng Ma, Guangxi Li, Yiyang Zhao, Wei Wu, Siqi Liang, Shandian Zhe and Tim Kraska and has published in prestigious journals such as Neurocomputing, Neural Networks and ACM SIGPLAN Notices.

In The Last Decade

Jinmian Ye

7 papers receiving 158 citations

Peers

Jinmian Ye

AI

CVPR

CM

HA

EEE

Yinchong Yang Germany

Sharan Narang United States

Dmitry Molchanov Russia

Chenqian Yan United Kingdom

Sehoon Kim United States

Yury Nahshan Israel

Zhuoran Song China

Jure Sokolić United Kingdom

Vadim Sheinin United States

Yinchong Yang Germany

Jinmian Ye

65 ×0.7

AI

40 ×0.5

CVPR

38 ×1.5

CM

10 ×0.5

HA

12 ×0.7

EEE

Citations per year, relative to Jinmian Ye Jinmian Ye (= 1×) peers Yinchong Yang

Countries citing papers authored by Jinmian Ye

Since Specialization

Citations

This map shows the geographic impact of Jinmian Ye's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jinmian Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jinmian Ye more than expected).

Fields of papers citing papers by Jinmian Ye

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jinmian Ye. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jinmian Ye. The network helps show where Jinmian Ye may publish in the future.

Co-authorship network of co-authors of Jinmian Ye

This figure shows the co-authorship network connecting the top 25 collaborators of Jinmian Ye. A scholar is included among the top collaborators of Jinmian Ye based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jinmian Ye. Jinmian Ye is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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7 of 7 papers shown

1.

Ye, Jinmian, Guangxi Li, Di Chen, et al.. (2020). Block-term tensor neural networks. Neural Networks. 130. 11–21. 17 indexed citations

2.

Yan, Kun, et al.. (2019). Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks. arXiv (Cornell University). 32. 2133–2144. 45 indexed citations

3.

Ye, Jinmian, et al.. (2019). Adversarial Noise Layer: Regularize Neural Network by Adding Noise. 909–913. 47 indexed citations

4.

Liang, Siqi, et al.. (2018). TensorD: A tensor decomposition library in TensorFlow. Neurocomputing. 318. 196–200. 17 indexed citations

5.

Wang, Linnan, Jinmian Ye, Yiyang Zhao, et al.. (2018). Superneurons. ACM SIGPLAN Notices. 53(1). 41–53. 22 indexed citations

6.

Zhao, Yiyang, Linnan Wang, Wei Wu, et al.. (2017). Efficient Communications in Training Large Scale Neural Networks. 110–116. 8 indexed citations

7.

Li, Guangxi, et al.. (2017). Simple and efficient parallelization for probabilistic temporal tensor factorization. 1–8. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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