Patrick van der Smagt
- Cognitive Neuroscience top 0.5%
- Computer Vision and Pattern Recognition top 0.2%
- Biomedical Engineering top 1%
- Cellular and Molecular Neuroscience top 1%
- Artificial Intelligence top 1%
- Co-authors
- Eddy IlgCaner HazırbaşThomas BroxPhilipp FischerDaniel CremersAlexey DosovitskiyVladimir GolkovClaudio Castellini
- Topics
- Muscle activation and electromyography studies (38 papers)Robot Manipulation and Learning (29 papers)EEG and Brain-Computer Interfaces (16 papers)
- Partner nations
- GermanyUnited StatesNetherlands
In The Last Decade
Patrick van der Smagt
98 papers receiving 7.0k citations
Hit Papers
Peers
Comparison fields: 5 of 164
- Cognitive Neuroscience 2.5k
- Computer Vision and Pattern Recognition 2.4k
- Biomedical Engineering 2.3k
- Cellular and Molecular Neuroscience 1.7k
- Artificial Intelligence 970
Countries citing papers authored by Patrick van der Smagt
This map shows the geographic impact of Patrick van der Smagt'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 Patrick van der Smagt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Patrick van der Smagt more than expected).
Fields of papers citing papers by Patrick van der Smagt
This network shows the impact of papers produced by Patrick van der Smagt. 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 Patrick van der Smagt. The network helps show where Patrick van der Smagt may publish in the future.
Co-authorship network of co-authors of Patrick van der Smagt
This figure shows the co-authorship network connecting the top 25 collaborators of Patrick van der Smagt. A scholar is included among the top collaborators of Patrick van der Smagt 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 Patrick van der Smagt. Patrick van der Smagt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 5 | |
| 4 | 6 | |
| 5 | 1 | |
| 6 | 6 | |
| 7 | 3 | |
| 8 | Continual Learning with Bayesian Neural Networks for Non-Stationary Data | 16 |
| 9 | Metrics for Deep Generative Models | 6 |
| 10 | 38 | |
| 11 | 15 | |
| 12 | 22 | |
| 13 | 2 | |
| 14 | 16 | |
| 15 | 0 | |
| 16 | 14 | |
| 17 | Surface EMG suffices to classify the motion of each finger independently | 17 |
| 18 | 332 | |
| 19 | Neural Networks for Robotics | 1 |
| 20 | Nested Networks For Robot Control | 1 |
About Patrick van der Smagt
Patrick van der Smagt is a scholar working on Control and Systems Engineering, Cognitive Neuroscience and Human-Computer Interaction, having authored 106 papers that have together received 7.3k indexed citations. Recurring topics across this work include Muscle activation and electromyography studies (38 papers), Robot Manipulation and Learning (29 papers) and EEG and Brain-Computer Interfaces (16 papers). The work is most often cited by research in Cognitive Neuroscience (2.5k citations), Computer Vision and Pattern Recognition (2.4k citations) and Human-Computer Interaction (531 citations). Patrick van der Smagt has collaborated with scholars based in Germany, United States and Netherlands. Frequent co-authors include Eddy Ilg, Caner Hazırbaş, Thomas Brox, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, Vladimir Golkov, Claudio Castellini, Jörn Vogel and Sami Haddadin. Their work appears in journals such as Nature, PLoS ONE and The Astrophysical Journal.
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.