Tim G. J. Rudner
- Artificial Intelligence
- Media Technology top 10%
- Global and Planetary Change
- Computer Vision and Pattern Recognition
- Atmospheric Science
- Co-authors
- Piotr BilińskiMarc RußwurmVeronika KopačkováBenjamin BischkeRamona PelichYarin GalShimon WhitesonGregory Farquhar
- Topics
- Reinforcement Learning in Robotics (3 papers)Gaussian Processes and Bayesian Inference (3 papers)Anomaly Detection Techniques and Applications (2 papers)
- Journals
- EntropyOxford University Research Archive (ORA) (University of Oxford)arXiv (Cornell University)
- Partner nations
- United KingdomUnited StatesLuxembourg
In The Last Decade
Tim G. J. Rudner
9 papers receiving 90 citations
Peers
Comparison fields: 5 of 42
- Artificial Intelligence 36
- Media Technology 33
- Global and Planetary Change 32
- Computer Vision and Pattern Recognition 20
- Atmospheric Science 19
Countries citing papers authored by Tim G. J. Rudner
This map shows the geographic impact of Tim G. J. Rudner'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 Tim G. J. Rudner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim G. J. Rudner more than expected).
Fields of papers citing papers by Tim G. J. Rudner
This network shows the impact of papers produced by Tim G. J. Rudner. 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 Tim G. J. Rudner. The network helps show where Tim G. J. Rudner may publish in the future.
Co-authorship network of co-authors of Tim G. J. Rudner
This figure shows the co-authorship network connecting the top 25 collaborators of Tim G. J. Rudner. A scholar is included among the top collaborators of Tim G. J. Rudner based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tim G. J. Rudner. Tim G. J. Rudner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 3 | |
| 3 | 1 | |
| 4 | On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes | 1 |
| 5 | Inter-domain Deep Gaussian Processes | 3 |
| 6 | Rethinking Function-Space Variational Inference in Bayesian Neural Networks | 1 |
| 7 | 5 | |
| 8 | VIREL: A Variational Inference Framework for Reinforcement Learning | 4 |
| 9 | 73 |
About Tim G. J. Rudner
Tim G. J. Rudner is a scholar working on Artificial Intelligence, Media Technology and Control and Systems Engineering, having authored 9 papers that have together received 93 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Gaussian Processes and Bayesian Inference (3 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Media Technology (33 citations), Global and Planetary Change (32 citations) and Artificial Intelligence (36 citations). Tim G. J. Rudner has collaborated with scholars based in United Kingdom, United States and Luxembourg. Frequent co-authors include Piotr Biliński, Marc Rußwurm, Veronika Kopačková, Benjamin Bischke, Ramona Pelich, Yarin Gal, Shimon Whiteson, Gregory Farquhar, Jakob Foerster and Adam D. Cobb. Their work appears in journals such as Entropy, Oxford University Research Archive (ORA) (University of Oxford) 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.