Hippolyt Ritter
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Computational Mechanics
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- David BarberAleksandar BotevGeorge KesidisDavid J. MillerZhen XiangJiahui ChenHang WangOusmane Dia
- Topics
- Multimodal Machine Learning Applications (3 papers)Domain Adaptation and Few-Shot Learning (3 papers)Neural Networks and Applications (2 papers)
- Journals
- arXiv (Cornell University)UCL Discovery (University College London)
- Partner nations
- United KingdomUnited States
In The Last Decade
Hippolyt Ritter
6 papers receiving 87 citations
Peers
Comparison fields: 5 of 30
- Artificial Intelligence 87
- Computer Vision and Pattern Recognition 42
- Control and Systems Engineering 7
- Computational Mechanics 7
- Radiology, Nuclear Medicine and Imaging 7
Countries citing papers authored by Hippolyt Ritter
This map shows the geographic impact of Hippolyt Ritter'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 Hippolyt Ritter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hippolyt Ritter more than expected).
Fields of papers citing papers by Hippolyt Ritter
This network shows the impact of papers produced by Hippolyt Ritter. 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 Hippolyt Ritter. The network helps show where Hippolyt Ritter may publish in the future.
Co-authorship network of co-authors of Hippolyt Ritter
This figure shows the co-authorship network connecting the top 25 collaborators of Hippolyt Ritter. A scholar is included among the top collaborators of Hippolyt Ritter 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 Hippolyt Ritter. Hippolyt Ritter is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | Bayesian Online Meta-Learning with Laplace Approximation. | 1 |
| 3 | 1 | |
| 4 | A Scalable Laplace Approximation for Neural Networks | 51 |
| 5 | Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting | 26 |
| 6 | Noisy Information Bottlenecks for Generalization | 0 |
| 7 | Practical Gauss-Newton Optimisation for Deep Learning | 19 |
About Hippolyt Ritter
Hippolyt Ritter is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition, having authored 7 papers that have together received 99 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (3 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Computational Mathematics (2 citations), Artificial Intelligence (87 citations) and Computer Vision and Pattern Recognition (42 citations). Hippolyt Ritter has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include David Barber, Aleksandar Botev, George Kesidis, David J. Miller, Zhen Xiang, Jiahui Chen, Hang Wang and Ousmane Dia. Their work appears in journals such as arXiv (Cornell University) and UCL Discovery (University College London).
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.