Jake Snell
Impact in
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- Generative Adversarial Networks and Image Synthesis
- Advanced Image Processing Techniques
- Face recognition and analysis
- Advanced Vision and Imaging
- Digital Media Forensic Detection
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- Advanced Graph Neural Networks
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
Papers in
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- Neural Networks and Applications 2
- Adversarial Robustness in Machine Learning 1
- Anomaly Detection Techniques and Applications 1
- Statistical and Computational Modeling 1
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- Generative Adversarial Networks and Image Synthesis 1
- Advanced Image Processing Techniques 1
- Co-authors
- Richard S. Zemel (5 shared papers)Renjie Liao (2 shared papers)Brett D. Roads (1 shared paper)Michael C. Mozer (1 shared paper)Marc T. Law (3 shared papers)Raquel Urtasun (1 shared paper)Amir‐massoud Farahmand (1 shared paper)
- Journals
- Uncertainty in Artificial Intelligence (1 paper)International Conference on Learning Representations (1 paper)International Conference on Machine Learning (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- CanadaUnited States
In The Last Decade
Jake Snell
3 papers receiving 59 citations
Peers
Comparison fields: 5 of 29
- Computer Vision and Pattern Recognition 36
- Artificial Intelligence 23
- Statistical and Nonlinear Physics 5
- Biophysics 2
- Signal Processing 4
Countries citing papers authored by Jake Snell
This map shows the geographic impact of Jake Snell'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 Jake Snell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jake Snell more than expected).
Fields of papers citing papers by Jake Snell
This network shows the impact of papers produced by Jake Snell. 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 Jake Snell. The network helps show where Jake Snell may publish in the future.
Co-authors
The 7 scholars most cited alongside Jake Snell, 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 | 2017 | 35 | |
| 2 | Lorentzian Distance Learning for Hyperbolic Representations | 2019 | 23 |
| 3 | Dimensionality Reduction for Representing the Knowledge of Probabilistic Models | 2018 | 3 |
| 4 | Stochastic Segmentation Trees for Multiple Ground Truths. | 2017 | 0 |
| 5 | Lorentzian Distance Learning | 2018 | 0 |
About Jake Snell
Jake Snell is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Biophysics, Media Technology and Signal Processing, having authored 5 papers that have together received 61 indexed citations. Recurring topics across this work include Neural Networks and Applications (2 papers), Cell Image Analysis Techniques (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Adversarial Robustness in Machine Learning (1 paper), Anomaly Detection Techniques and Applications (1 paper), Time Series Analysis and Forecasting (1 paper), Advanced Image Processing Techniques (1 paper) and Statistical and Computational Modeling (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (36 citations), Artificial Intelligence (23 citations), Statistical and Nonlinear Physics (5 citations), Biophysics (2 citations) and Signal Processing (4 citations). Jake Snell has collaborated with scholars based in Canada and United States. Frequent co-authors include Richard S. Zemel, Renjie Liao, Brett D. Roads, Michael C. Mozer, Marc T. Law, Raquel Urtasun and Amir‐massoud Farahmand. Their work appears in journals such as Uncertainty in Artificial Intelligence, International Conference on Learning Representations, International Conference on Machine Learning and arXiv (Cornell University).
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.