Paul Vicol
Impact in
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- Anomaly Detection Techniques and Applications
- Adversarial Robustness in Machine Learning
- Natural Language Processing Techniques
- Topic Modeling
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- Generative Adversarial Networks and Image Synthesis
- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
Papers in
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- Adversarial Robustness in Machine Learning 3
- Neural Networks and Applications 2
- Stochastic Gradient Optimization Techniques 2
- Anomaly Detection Techniques and Applications 2
- Evolutionary Algorithms and Applications 1
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- Advanced Neural Network Applications 3
- Co-authors
- Roger Grosse (5 shared papers)Jimmy Ba (2 shared papers)Richard S. Zemel (2 shared papers)James Lucas (1 shared paper)David Duvenaud (1 shared paper)Kuan-Chieh Wang (3 shared papers)Yeming Wen (1 shared paper)Dustin Tran (1 shared paper)
- Journals
- The Fibonacci quarterly (1 paper)Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (1 paper)arXiv (Cornell University) (3 papers)International Conference on Machine Learning (1 paper)
- Partner nations
- CanadaUnited States
In The Last Decade
Paul Vicol
6 papers receiving 22 citations
Peers
Comparison fields: 5 of 24
- Artificial Intelligence 12
- Computer Vision and Pattern Recognition 7
- Numerical Analysis 1
- Control and Systems Engineering 3
- Statistics and Probability 1
Countries citing papers authored by Paul Vicol
This map shows the geographic impact of Paul Vicol'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 Paul Vicol with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paul Vicol more than expected).
Fields of papers citing papers by Paul Vicol
This network shows the impact of papers produced by Paul Vicol. 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 Paul Vicol. The network helps show where Paul Vicol may publish in the future.
Co-authors
The 14 scholars most cited alongside Paul Vicol, 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 | Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches | 2018 | 6 |
| 2 | 2019 | 6 | |
| 3 | 2018 | 4 | |
| 4 | Adversarial Distillation of Bayesian Neural Network Posteriors | 2018 | 3 |
| 5 | Out-of-distribution Detection in Few-shot Classification | 2019 | 2 |
| 6 | On the Invertibility of Invertible Neural Networks | 2019 | 1 |
| 7 | 2017 | 0 | |
| 8 | 2022 | 0 |
About Paul Vicol
Paul Vicol is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Mathematical Physics and Statistical and Nonlinear Physics, having authored 8 papers that have together received 22 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Advanced Neural Network Applications (3 papers), Neural Networks and Applications (2 papers), Stochastic Gradient Optimization Techniques (2 papers), Anomaly Detection Techniques and Applications (2 papers), Time Series Analysis and Forecasting (1 paper), Evolutionary Algorithms and Applications (1 paper) and Advanced Mathematical Theories and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (12 citations), Computer Vision and Pattern Recognition (7 citations), Numerical Analysis (1 citation), Control and Systems Engineering (3 citations) and Statistics and Probability (1 citation). Paul Vicol has collaborated with scholars based in Canada and United States. Frequent co-authors include Roger Grosse, Jimmy Ba, Richard S. Zemel, James Lucas, David Duvenaud, Kuan-Chieh Wang, Yeming Wen, Dustin Tran, Li Gu and Eleni Triantafillou. Their work appears in journals such as The Fibonacci quarterly, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 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.