Rewon Child
Impact in
- Signal Processing top 5%
- Music and Audio Processing
- Speech and Audio Processing
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- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Generative Adversarial Networks and Image Synthesis
Papers in
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- Speech and Audio Processing 2
- Music and Audio Processing 2
-
- Speech Recognition and Synthesis 3
- Co-authors
- Heewoo Jun (2 shared papers)Alec Radford (2 shared papers)Mark I-Cheng Chen (2 shared papers)Ilya Sutskever (2 shared papers)Jeffrey Wu (1 shared paper)Adam Coates (2 shared papers)Anuroop Sriram (1 shared paper)Zhenyao Zhu (1 shared paper)
- Journals
- Acta Horticulturae (1 paper)International Conference on Machine Learning (2 papers)
- Partner nations
- United StatesChina
In The Last Decade
Rewon Child
6 papers receiving 585 citations
Rewon Child's Hit Papers
Peers
Comparison fields: 5 of 81
- Signal Processing 215
- Computer Vision and Pattern Recognition 281
- Artificial Intelligence 393
- Computer Graphics and Computer-Aided Design 19
- Media Technology 22
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 19 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 | 326 |
| 2 | 2017 | 158 | |
| 3 | 2017 | 114 | |
| 4 | 2019 | 15 | |
| 5 | Distribution Augmentation for Generative Modeling | 2020 | 9 |
| 6 | 1981 | 1 |
About Rewon Child
Rewon Child is a scholar working on Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition, Plant Science and Mechanical Engineering, having authored 6 papers that have together received 623 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (3 papers), Speech and Audio Processing (2 papers), Music and Audio Processing (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Plant Physiology and Cultivation Studies (1 paper), Horticultural and Viticultural Research (1 paper) and Tree Root and Stability Studies (1 paper). The work is most often cited by research in Signal Processing (215 citations), Computer Vision and Pattern Recognition (281 citations), Artificial Intelligence (393 citations), Computer Graphics and Computer-Aided Design (19 citations) and Media Technology (22 citations). Rewon Child has collaborated with scholars based in United States and China. Frequent co-authors include Heewoo Jun, Alec Radford, Mark I-Cheng Chen, Ilya Sutskever, Jeffrey Wu, Adam Coates, Anuroop Sriram, Zhenyao Zhu, Hairong Liu and Jitong Chen. 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.