Jake Snell
Impact in
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- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
- Generative Adversarial Networks and Image Synthesis
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
- Advanced Graph Neural Networks
- Machine Learning and ELM
Papers in
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- Neural Networks and Applications 2
- Statistical and Computational Modeling 1
- Machine Learning and Data Classification 1
- Gaussian Processes and Bayesian Inference 1
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- Generative Adversarial Networks and Image Synthesis 2
- Face recognition and analysis 1
- Co-authors
- Richard S. Zemel (5 shared papers)Mengye Ren (1 shared paper)Eleni Triantafillou (1 shared paper)Kevin Swersky (1 shared paper)Josh Tenenbaum (1 shared paper)Hugo Larochelle (1 shared paper)Sachin Ravi (1 shared paper)Renjie Liao (1 shared paper)
- Journals
- International Conference on Learning Representations (1 paper)arXiv (Cornell University) (2 papers)International Conference on Machine Learning (1 paper)
- Partner nations
- CanadaUnited States
In The Last Decade
Jake Snell
4 papers receiving 114 citations
Peers
Comparison fields: 5 of 36
- Computer Vision and Pattern Recognition 79
- Artificial Intelligence 105
- Structural Biology 1
- Cancer Research 10
- Radiology, Nuclear Medicine and Imaging 15
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 11 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 | Meta-Learning for Semi-Supervised Few-Shot Classification | 2018 | 73 |
| 2 | 2018 | 23 | |
| 3 | Lorentzian Distance Learning for Hyperbolic Representations | 2019 | 22 |
| 4 | Dimensionality Reduction for Representing the Knowledge of Probabilistic Models | 2018 | 3 |
| 5 | Lorentzian Distance Learning | 2018 | 0 |
About Jake Snell
Jake Snell is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Computational Theory and Mathematics and General Social Sciences, having authored 5 papers that have together received 121 indexed citations. Recurring topics across this work include Neural Networks and Applications (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Statistical and Computational Modeling (1 paper), Topological and Geometric Data Analysis (1 paper), Machine Learning and Data Classification (1 paper), Time Series Analysis and Forecasting (1 paper), Face recognition and analysis (1 paper) and Gaussian Processes and Bayesian Inference (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (79 citations), Artificial Intelligence (105 citations), Structural Biology (1 citation), Cancer Research (10 citations) and Radiology, Nuclear Medicine and Imaging (15 citations). Jake Snell has collaborated with scholars based in Canada and United States. Frequent co-authors include Richard S. Zemel, Mengye Ren, Eleni Triantafillou, Kevin Swersky, Josh Tenenbaum, Hugo Larochelle, Sachin Ravi, Renjie Liao, Marc T. Law and Amir‐massoud Farahmand. Their work appears in journals such as 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.