Will Grathwohl
Impact in
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- Generative Adversarial Networks and Image Synthesis
- Advanced Neural Network Applications
Papers in ⓘ
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- Neural Networks and Applications 2
- Adversarial Robustness in Machine Learning 2
- Speech Recognition and Synthesis 1
- Computational Physics and Python Applications 1
- Stochastic Gradient Optimization Techniques 1
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- Generative Adversarial Networks and Image Synthesis 4
- Co-authors
- Joern-Henrik Jacobsen (2 shared papers)David Duvenaud (3 shared papers)Jens Behrmann (1 shared paper)Ricky T. Q. Chen (1 shared paper)Chase Cockrell (1 shared paper)Jiachen Yang (1 shared paper)Brenden K. Petersen (1 shared paper)Daniel Faissol (1 shared paper)
- Journals
- Journal of Computational Biology (1 paper)The Journal of the Acoustical Society of America (1 paper)International Conference on Learning Representations (1 paper)arXiv (Cornell University) (2 papers)International Conference on Machine Learning (2 papers)
- Partner nations
- CanadaUnited StatesGermany
In The Last Decade
Will Grathwohl
7 papers receiving 85 citations
Peers
Comparison fields: 5 of 52
- Health Informatics 6
- Computer Vision and Pattern Recognition 31
- Artificial Intelligence 48
- Statistical and Nonlinear Physics 13
- Biophysics 4
Countries citing papers authored by Will Grathwohl
This map shows the geographic impact of Will Grathwohl'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 Will Grathwohl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Will Grathwohl more than expected).
Fields of papers citing papers by Will Grathwohl
This network shows the impact of papers produced by Will Grathwohl. 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 Will Grathwohl. The network helps show where Will Grathwohl may publish in the future.
Co-authors
The 18 scholars most cited alongside Will Grathwohl, 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 | Invertible Residual Networks | 2019 | 40 |
| 2 | 2019 | 34 | |
| 3 | Gradient-based Optimization of Neural Network Architecture. | 2018 | 5 |
| 4 | 2019 | 5 | |
| 5 | Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling | 2020 | 3 |
| 6 | 2010 | 2 | |
| 7 | Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling | 2020 | 1 |
About Will Grathwohl
Will Grathwohl is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Molecular Biology and Epidemiology, having authored 7 papers that have together received 90 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (4 papers), Neural Networks and Applications (2 papers), Adversarial Robustness in Machine Learning (2 papers), Speech Recognition and Synthesis (1 paper), Speech and Audio Processing (1 paper), Phonetics and Phonology Research (1 paper), Computational Physics and Python Applications (1 paper) and Stochastic Gradient Optimization Techniques (1 paper). The work is most often cited by research in Health Informatics (6 citations), Computer Vision and Pattern Recognition (31 citations), Artificial Intelligence (48 citations), Statistical and Nonlinear Physics (13 citations) and Biophysics (4 citations). Will Grathwohl has collaborated with scholars based in Canada, United States and Germany. Frequent co-authors include Joern-Henrik Jacobsen, David Duvenaud, Jens Behrmann, Ricky T. Q. Chen, Chase Cockrell, Jiachen Yang, Brenden K. Petersen, Daniel Faissol, Gary An and Richard S. Zemel. Their work appears in journals such as Journal of Computational Biology, The Journal of the Acoustical Society of America, International Conference on Learning Representations, arXiv (Cornell University) 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.