Chao Ma

3.3k total citations · 1 hit paper
123 papers, 2.2k citations indexed

About

Chao Ma is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Chao Ma has authored 123 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Artificial Intelligence, 27 papers in Information Systems and 27 papers in Computer Vision and Pattern Recognition. Recurrent topics in Chao Ma's work include Topic Modeling (13 papers), Machine Learning and ELM (10 papers) and Service-Oriented Architecture and Web Services (9 papers). Chao Ma is often cited by papers focused on Topic Modeling (13 papers), Machine Learning and ELM (10 papers) and Service-Oriented Architecture and Web Services (9 papers). Chao Ma collaborates with scholars based in China, United States and Hong Kong. Chao Ma's co-authors include Huiling Chen, Xuehua Zhao, Xiaochuan Shi, Wei Li, Daoliang Li, Bo Yang, Yungang Zhu, Wenbin Liu, Gang Wang and Sujing Wang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Applied Energy and Expert Systems with Applications.

In The Last Decade

Chao Ma

112 papers receiving 2.2k 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.

Explainable artificial intelligence for digital finance and consumption upgrading

2023 146 citations Xiaochuan Shi, Lihua Gao et al. Finance research letters profile →

Peers

Chao Ma

AI

CVPR

IS

EEE

CNC

Jing Peng China

K. Venkatachalam Czechia

Amina Adadi Morocco

Hesham A. Hefny Egypt

Mohammed Berrada Morocco

Kewei Cheng United States

Serdar Korukoğlu Türkiye

Satchidananda Dehuri India

Gongbo Zhang United States

Wei‐Chao Lin Taiwan

Jing Peng China

Chao Ma

1.0k ×1.1

AI

573 ×1.3

CVPR

232 ×0.9

IS

162 ×0.8

EEE

228 ×1.3

CNC

Citations per year, relative to Chao Ma Chao Ma (= 1×) peers Jing Peng

Countries citing papers authored by Chao Ma

Since Specialization

Citations

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

Fields of papers citing papers by Chao Ma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chao Ma

This figure shows the co-authorship network connecting the top 25 collaborators of Chao Ma. A scholar is included among the top collaborators of Chao Ma 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 Chao Ma. Chao Ma 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:

20 of 20 papers shown

1.

Li, Zihao, Lixin Zou, Chao Ma, & Chenliang Li. (2025). Efficient and explainable sequential recommendation with language model. Information Processing & Management. 62(4). 104122–104122. 1 indexed citations

2.

Lıu, Mao, et al.. (2024). Multi-stage fully adaptive distributionally robust unit commitment for power system based on mixed approximation rules. Applied Energy. 376. 124051–124051. 3 indexed citations

3.

Ma, Chao & Ziyang Wang. (2024). Semi-Mamba-UNet: Pixel-level contrastive and cross-supervised visual Mamba-based UNet for semi-supervised medical image segmentation. Knowledge-Based Systems. 300. 112203–112203. 32 indexed citations

4.

Shi, Xiaochuan, et al.. (2023). Explainable artificial intelligence for digital finance and consumption upgrading. Finance research letters. 58. 104489–104489. 146 indexed citations breakdown →

5.

Wu, Libing, et al.. (2023). A Low-Overhead Auditing Protocol for Dynamic Cloud Storage Based on Algebra. Security and Communication Networks. 2023. 1–21. 1 indexed citations

6.

Li, Wei, Jinlin Chen, Jiannong Cao, et al.. (2022). EID-GAN: Generative Adversarial Nets for Extremely Imbalanced Data Augmentation. IEEE Transactions on Industrial Informatics. 19(3). 3208–3218. 49 indexed citations

7.

Cao, Jiannong, et al.. (2022). An agnostic and efficient approach to identifying features from execution traces. Knowledge-Based Systems. 250. 108988–108988. 1 indexed citations

8.

Ma, Chao, et al.. (2021). Salience-CAM: Visual Explanations from Convolutional Neural Networks via Salience Score. 1–8. 5 indexed citations

9.

Liu, Yun, Ali Asghar Heidari, Xiaojia Ye, et al.. (2021). Evolutionary shuffled frog leaping with memory pool for parameter optimization. Energy Reports. 7. 584–606. 39 indexed citations

10.

Ma, Chao, Xiaochuan Shi, Wei Li, & Weiping Zhu. (2020). Edge4TSC: Binary Distribution Tree-Enabled Time Series Classification in Edge Environment. Sensors. 20(7). 1908–1908. 8 indexed citations

11.

Ma, Chao, et al.. (2020). Optimal Type-3 Fuzzy System for Solving Singular Multi-Pantograph Equations. IEEE Access. 8. 225692–225702. 30 indexed citations

12.

Zhu, Wei, Chao Ma, Xuehua Zhao, et al.. (2020). Evaluation of Sino Foreign Cooperative Education Project Using Orthogonal Sine Cosine Optimized Kernel Extreme Learning Machine. IEEE Access. 8. 61107–61123. 69 indexed citations

13.

Weinan, E, Chao Ma, & Lei Wu. (2019). Barron Spaces and the Compositional Function Spaces for Neural Network Models.. arXiv (Cornell University). 27 indexed citations

14.

Zhang, Wenyue, et al.. (2019). Electricity Price Cross Subsidy Calculation Model Considering Load Characteristics of Electricity Consumers. 52(5). 148–154. 2 indexed citations

15.

Zhu, Chen, et al.. (2018). Person-Job Fit. ACM Transactions on Management Information Systems. 9(3). 1–17. 91 indexed citations

16.

Wu, Lei, Chao Ma, & E Weinan. (2018). How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective. Neural Information Processing Systems. 31. 8279–8288. 26 indexed citations

17.

Fern, Xiaoli Z., et al.. (2018). Joint Neural Entity Disambiguation with Output Space Search. International Conference on Computational Linguistics. 2170–2180. 1 indexed citations

18.

Ma, Chao, et al.. (2017). Select-and-Evaluate: A Learning Framework for Large-Scale Knowledge Graph Search. Asian Conference on Machine Learning. 129–144. 6 indexed citations

19.

Ma, Chao, et al.. (2017). Multi-Task Structured Prediction for Entity Analysis: Search-Based Learning Algorithms. Asian Conference on Machine Learning. 514–529. 2 indexed citations

20.

Ma, Chao. (2008). Application of Neural Network and IP Marking in DDoS Attack Defence. Jisuanji fangzhen. 2 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.

Explore authors with similar magnitude of impact