Rewon Child
Impact in
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- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Generative Adversarial Networks and Image Synthesis
- Advanced Image and Video Retrieval Techniques
- Signal Processing top 10%
- Music and Audio Processing
- Speech and Audio Processing
Papers in
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- Speech Recognition and Synthesis 2
- Natural Language Processing Techniques 1
- Topic Modeling 1
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- Advanced Image and Video Retrieval Techniques 1
- Generative Adversarial Networks and Image Synthesis 1
- Co-authors
- Mark Chen (2 shared papers)Ilya Sutskever (2 shared papers)Heewoo Jun (2 shared papers)Alec Radford (2 shared papers)Jeffrey Wu (1 shared paper)Sercan Ö. Arık (1 shared paper)Joel Hestness (1 shared paper)Ryan Prenger (1 shared paper)
- Journals
- Acta Horticulturae (1 paper)International Conference on Machine Learning (2 papers)
- Partner nations
- United States
In The Last Decade
Rewon Child
5 papers receiving 410 citations
Rewon Child's Hit Papers
Peers
Comparison fields: 5 of 76
- Computer Vision and Pattern Recognition 248
- Signal Processing 106
- Artificial Intelligence 229
- Computer Graphics and Computer-Aided Design 17
- Media Technology 21
Countries citing papers authored by Rewon Child
This map shows the geographic impact of Rewon Child'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 Rewon Child with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rewon Child more than expected).
Fields of papers citing papers by Rewon Child
This network shows the impact of papers produced by Rewon Child. 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 Rewon Child. The network helps show where Rewon Child may publish in the future.
Co-authors
The 13 scholars most cited alongside Rewon Child, 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 | Generative Pretraining From Pixels Hit paper breakdown → | 2020 | 300 |
| 2 | 2017 | 110 | |
| 3 | 2019 | 14 | |
| 4 | Distribution Augmentation for Generative Modeling | 2020 | 8 |
| 5 | 1981 | 1 |
About Rewon Child
Rewon Child is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Mechanical Engineering and Plant Science, having authored 5 papers that have together received 433 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (2 papers), Horticultural and Viticultural Research (1 paper), Advanced Image and Video Retrieval Techniques (1 paper), Natural Language Processing Techniques (1 paper), Plant Physiology and Cultivation Studies (1 paper), Tree Root and Stability Studies (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (248 citations), Signal Processing (106 citations), Artificial Intelligence (229 citations), Computer Graphics and Computer-Aided Design (17 citations) and Media Technology (21 citations). Rewon Child has collaborated with scholars based in United States. Frequent co-authors include Mark Chen, Ilya Sutskever, Heewoo Jun, Alec Radford, Jeffrey Wu, Sercan Ö. Arık, Joel Hestness, Ryan Prenger, Adam Coates and Andrew Gibiansky. Their work appears in journals such as Acta Horticulturae and International Conference on Machine Learning.
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.