Sebastian Seung
- Computer Vision and Pattern Recognition top 2%
- Media Technology top 2%
- Biophysics top 5%
- Artificial Intelligence
- Biomedical Engineering
- Co-authors
- Viren JainKevin L. BriggmanSrinivas C. TuragaMoritz HelmstaedterWinfried DenkRichard SchalekNarayanan KasthuriDaniel R. Berger
- Topics
- Medical Image Segmentation Techniques (2 papers)Remote-Sensing Image Classification (1 paper)Image and Signal Denoising Methods (1 paper)
- Journals
- Microscopy and MicroanalysisarXiv (Cornell University)Neural Information Processing Systems
- Partner nations
- United StatesGermany
In The Last Decade
Sebastian Seung
5 papers receiving 511 citations
Hit Papers
Peers
Comparison fields: 5 of 69
- Computer Vision and Pattern Recognition 397
- Media Technology 208
- Biophysics 53
- Artificial Intelligence 50
- Biomedical Engineering 45
Countries citing papers authored by Sebastian Seung
This map shows the geographic impact of Sebastian Seung'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 Sebastian Seung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sebastian Seung more than expected).
Fields of papers citing papers by Sebastian Seung
This network shows the impact of papers produced by Sebastian Seung. 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 Sebastian Seung. The network helps show where Sebastian Seung may publish in the future.
Co-authorship network of co-authors of Sebastian Seung
This figure shows the co-authorship network connecting the top 25 collaborators of Sebastian Seung. A scholar is included among the top collaborators of Sebastian Seung 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 Sebastian Seung. Sebastian Seung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | QXplore: Q-Learning Exploration by Maximizing Temporal Difference Error | 2 |
| 2 | 1 | |
| 3 | 21 | |
| 4 | Maximin affinity learning of image segmentation | 37 |
| 5 | Natural Image Denoising with Convolutional Networksbreakdown → | 476 |
About Sebastian Seung
Sebastian Seung is a scholar working on Structural Biology, Media Technology and Surfaces, Coatings and Films, having authored 5 papers that have together received 537 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (2 papers), Remote-Sensing Image Classification (1 paper) and Image and Signal Denoising Methods (1 paper). The work is most often cited by research in Media Technology (208 citations), Structural Biology (33 citations) and Computer Vision and Pattern Recognition (397 citations). Sebastian Seung has collaborated with scholars based in United States and Germany. Frequent co-authors include Viren Jain, Kevin L. Briggman, Srinivas C. Turaga, Moritz Helmstaedter, Winfried Denk, Richard Schalek, Narayanan Kasthuri, Daniel R. Berger, Kenneth J. Hayworth and Alyssa M. Wilson. Their work appears in journals such as Microscopy and Microanalysis, arXiv (Cornell University) and Neural Information Processing Systems.
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.