Caixia Kou

625 total citations · 1 hit paper
11 papers, 472 citations indexed

About

Caixia Kou is a scholar working on Computer Networks and Communications, Numerical Analysis and Computer Vision and Pattern Recognition. According to data from OpenAlex, Caixia Kou has authored 11 papers receiving a total of 472 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Computer Networks and Communications, 3 papers in Numerical Analysis and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Caixia Kou's work include Sparse and Compressive Sensing Techniques (3 papers), Advanced Optimization Algorithms Research (3 papers) and Retinal Imaging and Analysis (2 papers). Caixia Kou is often cited by papers focused on Sparse and Compressive Sensing Techniques (3 papers), Advanced Optimization Algorithms Research (3 papers) and Retinal Imaging and Analysis (2 papers). Caixia Kou collaborates with scholars based in China, Germany and Canada. Caixia Kou's co-authors include Yu‐Hong Dai, Zekuan Yu, Qing Xiang, Yanye Lu, Qiushi Ren, Wei Li, Wei Li, Wei Liang, Han Yang and H. Leib and has published in prestigious journals such as IEEE Access, IEEE Journal on Selected Areas in Communications and IEEE/ACM Transactions on Networking.

In The Last Decade

Caixia Kou

11 papers receiving 455 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A Nonlinear Conjugate Gradient Algorithm with an Optimal Property and an Improved Wolfe Line Search

2013 236 citations Yu‐Hong Dai, Caixia Kou SIAM Journal on Optimization profile →

Peers

Caixia Kou

NA

CM

RNMI

CTM

CVPR

Agnieszka Lisowska Poland

L. K. Balyan India

Nicholas Assimakis Greece

Ch. Dossal France

Margherita Porcelli Italy

Yura Malitsky Sweden

Qiuwei Li United States

Feng Ma China

Doron Blatt United States

Damek Davis United States

Agnieszka Lisowska Poland

Caixia Kou

85 ×0.3

NA

26 ×0.2

CM

22 ×0.2

RNMI

31 ×0.3

CTM

127 ×1.1

CVPR

Citations per year, relative to Caixia Kou Caixia Kou (= 1×) peers Agnieszka Lisowska

Countries citing papers authored by Caixia Kou

Since Specialization

Citations

This map shows the geographic impact of Caixia Kou'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 Caixia Kou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Caixia Kou more than expected).

Fields of papers citing papers by Caixia Kou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Caixia Kou. 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 Caixia Kou. The network helps show where Caixia Kou may publish in the future.

Co-authorship network of co-authors of Caixia Kou

This figure shows the co-authorship network connecting the top 25 collaborators of Caixia Kou. A scholar is included among the top collaborators of Caixia Kou 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 Caixia Kou. Caixia Kou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

1.

Kou, Caixia & Han Yang. (2022). A mini-batch stochastic conjugate gradient algorithm with variance reduction. Journal of Global Optimization. 87(2-4). 1009–1025. 7 indexed citations

2.

Kou, Caixia, et al.. (2020). An Enhanced Residual U-Net for Microaneurysms and Exudates Segmentation in Fundus Images. IEEE Access. 8. 185514–185525. 38 indexed citations

3.

Yu, Zekuan, et al.. (2020). A New Deep Neural Architecture Search Pipeline for Face Recognition. IEEE Access. 8. 91303–91310. 23 indexed citations

4.

Yu, Zekuan, et al.. (2019). Retinal image synthesis from multiple-landmarks input with generative adversarial networks. BioMedical Engineering OnLine. 18(1). 62–62. 67 indexed citations

5.

Zhang, Pei, et al.. (2019). A Practical Traffic Control Scheme With Load Balancing Based on PCE Architecture. IEEE Access. 7. 30648–30658. 6 indexed citations

6.

Kou, Caixia, et al.. (2019). Microaneurysms segmentation with a U-Net based on recurrent residual convolutional neural network. Journal of Medical Imaging. 6(2). 1–1. 43 indexed citations

7.

Kou, Caixia, et al.. (2019). Bisection and Exact Algorithms Based on the Lagrangian Dual for a Single-Constrained Shortest Path Problem. IEEE/ACM Transactions on Networking. 28(1). 224–233. 5 indexed citations

8.

Dai, Yu‐Hong & Caixia Kou. (2016). A Barzilai-Borwein conjugate gradient method. Science China Mathematics. 59(8). 1511–1524. 32 indexed citations

9.

Kou, Caixia. (2014). An improved nonlinear conjugate gradient method with an optimal property. Science China Mathematics. 57(3). 635–648. 12 indexed citations

10.

Dai, Yu‐Hong & Caixia Kou. (2013). A Nonlinear Conjugate Gradient Algorithm with an Optimal Property and an Improved Wolfe Line Search. SIAM Journal on Optimization. 23(1). 296–320. 236 indexed citations breakdown →

11.

Kou, Caixia & H. Leib. (1996). Power imbalance effects on packet CDMA. IEEE Journal on Selected Areas in Communications. 14(9). 1830–1840. 3 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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