Joost van Amersfoort
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Cognitive Neuroscience
- Radiology, Nuclear Medicine and Imaging
- Control and Systems Engineering
- Co-authors
- Yarin GalAndreas KirschJishnu MukhotiPhilip H. S. TorrLewis SmithYee Whye TehAndrew J. QuinnUsama Pervaiz
- Topics
- Adversarial Robustness in Machine Learning (3 papers)Anomaly Detection Techniques and Applications (3 papers)Fault Detection and Control Systems (2 papers)
- Journals
- NeuroImagearXiv (Cornell University)International Conference on Machine Learning
- Partner nations
- United KingdomSwitzerland
In The Last Decade
Joost van Amersfoort
6 papers receiving 120 citations
Peers
Comparison fields: 5 of 43
- Artificial Intelligence 89
- Computer Vision and Pattern Recognition 22
- Cognitive Neuroscience 17
- Radiology, Nuclear Medicine and Imaging 16
- Control and Systems Engineering 14
Countries citing papers authored by Joost van Amersfoort
This map shows the geographic impact of Joost van Amersfoort'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 Joost van Amersfoort with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joost van Amersfoort more than expected).
Fields of papers citing papers by Joost van Amersfoort
This network shows the impact of papers produced by Joost van Amersfoort. 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 Joost van Amersfoort. The network helps show where Joost van Amersfoort may publish in the future.
Co-authorship network of co-authors of Joost van Amersfoort
This figure shows the co-authorship network connecting the top 25 collaborators of Joost van Amersfoort. A scholar is included among the top collaborators of Joost van Amersfoort 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 Joost van Amersfoort. Joost van Amersfoort is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 41 | |
| 2 | 17 | |
| 3 | Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression. | 7 |
| 4 | Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network | 9 |
| 5 | Uncertainty Estimation Using a Single Deep Deterministic Neural Network | 14 |
| 6 | BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning | 38 |
About Joost van Amersfoort
Joost van Amersfoort is a scholar working on Artificial Intelligence, Control and Systems Engineering and Cognitive Neuroscience, having authored 6 papers that have together received 126 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Anomaly Detection Techniques and Applications (3 papers) and Fault Detection and Control Systems (2 papers). The work is most often cited by research in Artificial Intelligence (89 citations), Health Informatics (2 citations) and Computer Vision and Pattern Recognition (22 citations). Joost van Amersfoort has collaborated with scholars based in United Kingdom and Switzerland. Frequent co-authors include Yarin Gal, Andreas Kirsch, Jishnu Mukhoti, Philip H. S. Torr, Lewis Smith, Yee Whye Teh, Andrew J. Quinn, Usama Pervaiz, Pascal Notin and Mark W. Woolrich. Their work appears in journals such as NeuroImage, 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.