Glenn Fung

77 papers receiving 3.4k citations

Hit Papers

Proximal support vector machine classifiers20012026200920172001200400600

Peers

Glenn Fung
Comparison fields: 5 of 167
  • Artificial Intelligence 2.2k
  • Computer Vision and Pattern Recognition 1.3k
  • Control and Systems Engineering 328
  • Molecular Biology 318
  • Computational Mechanics 306
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Countries citing papers authored by Glenn Fung

Since Specialization
Citations

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

Fields of papers citing papers by Glenn Fung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Glenn Fung

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1
Using Optimal Embeddings to Learn New Intents with Few Examples: An Application in the Insurance Domain.
2
2
Rationale-based Human-in-the-Loop via Supervised Attention.
1
3 15
4 7
5 75
6 25
7
Active Learning from Multiple Knowledge Sources
27
8
Building Hospital-Specific Readmission Risk Prediction Models for Heart Failure, Acute Myocardial Infarction and Pneumonia patients.
2
9
Modeling multiple annotator expertise in the semi-supervised learning scenario
20
10
Modeling annotator expertise: Learning when everybody knows a bit of something
113
11 12
12 10
13 30
14 143
15
Automated heart wall motion abnormality detection from ultrasound images using Bayesian networks
33
16
Feature selection and kernel design via linear programming
6
17 48
18
Learning Rankings via Convex Hull Separation
22
19
Knowledge-Based Support Vector Machine Classifiers
101
20 90

About Glenn Fung

Glenn Fung is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Science Applications, having authored 79 papers that have together received 3.6k indexed citations. Recurring topics across this work include Face and Expression Recognition (15 papers), Machine Learning and Data Classification (11 papers) and Machine Learning and Algorithms (9 papers). The work is most often cited by research in Artificial Intelligence (2.2k citations), Computer Vision and Pattern Recognition (1.3k citations) and Computer Science Applications (286 citations). Glenn Fung has collaborated with scholars based in United States, Germany and Netherlands. Frequent co-authors include O. L. Mangasarian, Rómer Rosales, Jennifer Dy, Jonathan Stoeckel, R. Bharat Rao, Yan Yan, Yan Yan, Balaji Krishnapuram, Mahdokht Masaeli and Jude Shavlik. Their work appears in journals such as PLoS ONE, International Journal of Radiation Oncology*Biology*Physics and IEEE Transactions on Biomedical Engineering.

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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