Linus Ericsson
Impact in
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Speech Recognition and Synthesis
- Anomaly Detection Techniques and Applications
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- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
Papers in
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- Topic Modeling 2
- Domain Adaptation and Few-Shot Learning 2
- Multi-Agent Systems and Negotiation 1
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- Multimodal Machine Learning Applications 1
- Advanced Image and Video Retrieval Techniques 1
- Advanced Neural Network Applications 1
- Co-authors
- Henry Gouk (1 shared paper)Timothy M. Hospedales (1 shared paper)Chen Change Loy (1 shared paper)Steven McDonagh (1 shared paper)Nanqing Dong (1 shared paper)Yongxin Yang (1 shared paper)Aleš Leonardis (1 shared paper)Jussi Karlgren (1 shared paper)
- Journals
- IEEE Signal Processing Magazine (1 paper)Neurocomputing (1 paper)KTH Publication Database DiVA (KTH Royal Institute of Technology) (1 paper)
- Partner nations
- United KingdomSwedenSingapore
In The Last Decade
Linus Ericsson
3 papers receiving 224 citations
Linus Ericsson's Hit Papers
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 104
- Computer Vision and Pattern Recognition 61
- Signal Processing 26
- Health Informatics 3
- Media Technology 20
Countries citing papers authored by Linus Ericsson
This map shows the geographic impact of Linus Ericsson'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 Linus Ericsson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Linus Ericsson more than expected).
Fields of papers citing papers by Linus Ericsson
This network shows the impact of papers produced by Linus Ericsson. 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 Linus Ericsson. The network helps show where Linus Ericsson may publish in the future.
Co-authors
The 8 scholars most cited alongside Linus Ericsson, 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 | Self-Supervised Representation Learning: Introduction, advances, and challenges Hit paper breakdown → | 2022 | 223 |
| 2 | 2024 | 5 | |
| 3 | Semantic Space Models for Profiling Reputation of Corporate Entities | 2013 | 1 |
About Linus Ericsson
Linus Ericsson is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Infectious Diseases and Organic Chemistry, having authored 3 papers that have together received 229 indexed citations. Recurring topics across this work include Topic Modeling (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Multimodal Machine Learning Applications (1 paper), Ferroelectric and Negative Capacitance Devices (1 paper), Advanced Image and Video Retrieval Techniques (1 paper), Advanced Neural Network Applications (1 paper) and Multi-Agent Systems and Negotiation (1 paper). The work is most often cited by research in Artificial Intelligence (104 citations), Computer Vision and Pattern Recognition (61 citations), Signal Processing (26 citations), Health Informatics (3 citations) and Media Technology (20 citations). Linus Ericsson has collaborated with scholars based in United Kingdom, Sweden and Singapore. Frequent co-authors include Henry Gouk, Timothy M. Hospedales, Chen Change Loy, Steven McDonagh, Nanqing Dong, Yongxin Yang, Aleš Leonardis and Jussi Karlgren. Their work appears in journals such as IEEE Signal Processing Magazine, Neurocomputing and KTH Publication Database DiVA (KTH Royal Institute of Technology).
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.