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
- Stochastic Gradient Optimization Techniques 1
- Adversarial Robustness in Machine Learning 1
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- Generative Adversarial Networks and Image Synthesis 2
- Co-authors
- David Duvenaud (2 shared papers)Jens Behrmann (1 shared paper)Ricky T. Q. Chen (1 shared paper)Brenden K. Petersen (1 shared paper)Daniel Faissol (1 shared paper)Robert Chase Cockrell (1 shared paper)Gary C. An (1 shared paper)Jia‐Cheng E. Yang (1 shared paper)
- Journals
- Journal of Computational Biology (1 paper)International Conference on Machine Learning (2 papers)International Conference on Learning Representations (1 paper)
- Partner nations
- United StatesCanadaGermany
In The Last Decade
Will Grathwohl
4 papers receiving 101 citations
Peers
Comparison fields: 5 of 49
- Health Informatics 6
- Computer Vision and Pattern Recognition 38
- Artificial Intelligence 51
- Statistical and Nonlinear Physics 13
- Modeling and Simulation 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 13 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 | 54 |
| 2 | 2019 | 41 | |
| 3 | Gradient-based Optimization of Neural Network Architecture. | 2018 | 6 |
| 4 | Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling | 2020 | 3 |
About Will Grathwohl
Will Grathwohl is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Epidemiology and Molecular Biology, having authored 4 papers that have together received 104 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (2 papers), Neural Networks and Applications (2 papers), Stochastic Gradient Optimization Techniques (1 paper), Music and Audio Processing (1 paper), Receptor Mechanisms and Signaling (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Sepsis Diagnosis and Treatment (1 paper). The work is most often cited by research in Health Informatics (6 citations), Computer Vision and Pattern Recognition (38 citations), Artificial Intelligence (51 citations), Statistical and Nonlinear Physics (13 citations) and Modeling and Simulation (4 citations). Will Grathwohl has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include David Duvenaud, Jens Behrmann, Ricky T. Q. Chen, Brenden K. Petersen, Daniel Faissol, Robert Chase Cockrell, Gary C. An, Jia‐Cheng E. Yang, Richard S. Zemel and Elliot Creager. Their work appears in journals such as Journal of Computational Biology, International Conference on Machine Learning and International Conference on Learning Representations.
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.