Philipp Gysel
Impact in
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- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
Papers in
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- Advanced Neural Network Applications 3
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- Anomaly Detection Techniques and Applications 2
- Adversarial Robustness in Machine Learning 2
- Machine Learning and ELM 1
- Co-authors
- Soheil Ghiasi (3 shared papers)Mohammad Motamedi (3 shared papers)Jon J. Pimentel (1 shared paper)Venkatesh Akella (1 shared paper)Dinil Mon Divakaran (2 shared papers)Mohan Gurusamy (1 shared paper)A. Ustyuzhanin (1 shared paper)
- Journals
- IEEE Transactions on Neural Networks and Learning Systems (1 paper)ACM Transactions on Multimedia Computing Communications and Applications (1 paper)
- Partner nations
- United StatesSingapore
In The Last Decade
Philipp Gysel
5 papers receiving 344 citations
Peers
Comparison fields: 5 of 46
- Computer Vision and Pattern Recognition 252
- Computational Mathematics 5
- Hardware and Architecture 47
- Artificial Intelligence 124
- Electrical and Electronic Engineering 197
Countries citing papers authored by Philipp Gysel
This map shows the geographic impact of Philipp Gysel'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 Philipp Gysel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philipp Gysel more than expected).
Fields of papers citing papers by Philipp Gysel
This network shows the impact of papers produced by Philipp Gysel. 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 Philipp Gysel. The network helps show where Philipp Gysel may publish in the future.
Co-authors
The 7 scholars most cited alongside Philipp Gysel, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 175 | |
| 2 | 2016 | 149 | |
| 3 | 2017 | 19 | |
| 4 | 2024 | 4 | |
| 5 | 2024 | 3 |
About Philipp Gysel
Philipp Gysel is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Information Systems and Management and Hardware and Architecture, having authored 5 papers that have together received 350 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (3 papers), Anomaly Detection Techniques and Applications (2 papers), Adversarial Robustness in Machine Learning (2 papers), Digital and Cyber Forensics (1 paper), Scientific Computing and Data Management (1 paper), Parallel Computing and Optimization Techniques (1 paper), Industrial Vision Systems and Defect Detection (1 paper) and Machine Learning and ELM (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (252 citations), Computational Mathematics (5 citations), Hardware and Architecture (47 citations), Artificial Intelligence (124 citations) and Electrical and Electronic Engineering (197 citations). Philipp Gysel has collaborated with scholars based in United States and Singapore. Frequent co-authors include Soheil Ghiasi, Mohammad Motamedi, Jon J. Pimentel, Venkatesh Akella, Dinil Mon Divakaran, Mohan Gurusamy and A. Ustyuzhanin. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems and ACM Transactions on Multimedia Computing Communications and Applications.
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.