William Fedus
Impact in
- Biophysics top 1%
- Cell Image Analysis Techniques
- Advanced Fluorescence Microscopy Techniques
- Media Technology top 5%
- Image Processing Techniques and Applications
Papers in ⓘ
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- Natural Language Processing Techniques 3
- Domain Adaptation and Few-Shot Learning 2
- Artificial Intelligence in Games 1
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- Cell Image Analysis Techniques 1
- Co-authors
- Andrew M. Dai (2 shared papers)Ian Goodfellow (2 shared papers)Yoshua Bengio (2 shared papers)Steven Finkbeiner (1 shared paper)Lee L. Rubin (1 shared paper)Philip Nelson (1 shared paper)Piyush Goyal (1 shared paper)D. Michael Ando (1 shared paper)
- Journals
- Cell (1 paper)International Conference on Learning Representations (2 papers)arXiv (Cornell University) (4 papers)Apollo (University of Cambridge) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
William Fedus
10 papers receiving 724 citations
Hit Papers
Peers
Comparison fields: 5 of 109
- Biophysics 255
- Media Technology 99
- Artificial Intelligence 353
- Computer Vision and Pattern Recognition 179
- Health Informatics 7
Countries citing papers authored by William Fedus
This map shows the geographic impact of William Fedus'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 William Fedus with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William Fedus more than expected).
Fields of papers citing papers by William Fedus
This network shows the impact of papers produced by William Fedus. 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 William Fedus. The network helps show where William Fedus may publish in the future.
Co-authors
The 25 scholars most cited alongside William Fedus, 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 | In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images Hit paper breakdown → | 2018 | 394 |
| 2 | 2018 | 118 | |
| 3 | MaskGAN: Better Text Generation via Filling in the ____ | 2018 | 95 |
| 4 | Language GANs Falling Short | 2020 | 34 |
| 5 | 2020 | 32 | |
| 6 | Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step | 2018 | 26 |
| 7 | 2022 | 25 | |
| 8 | 2023 | 18 | |
| 9 | 2021 | 3 | |
| 10 | Revisiting ResNets: Improved Training and Scaling Strategies | 2021 | 2 |
About William Fedus
William Fedus is a scholar working on Artificial Intelligence, Biophysics, Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition and Information Systems, having authored 10 papers that have together received 747 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (3 papers), Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Complex Network Analysis Techniques (1 paper), Complex Systems and Time Series Analysis (1 paper), Cell Image Analysis Techniques (1 paper), Artificial Intelligence in Games (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Biophysics (255 citations), Media Technology (99 citations), Artificial Intelligence (353 citations), Computer Vision and Pattern Recognition (179 citations) and Health Informatics (7 citations). William Fedus has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Andrew M. Dai, Ian Goodfellow, Yoshua Bengio, Steven Finkbeiner, Lee L. Rubin, Philip Nelson, Piyush Goyal, D. Michael Ando, Marc Berndl and Alison O’Neil. Their work appears in journals such as Cell, International Conference on Learning Representations, arXiv (Cornell University), Apollo (University of Cambridge) and Neural Information Processing Systems.
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.