Jan Koutník
- Artificial Intelligence top 0.5%
- Electrical and Electronic Engineering top 5%
- Computer Vision and Pattern Recognition top 1%
- Signal Processing top 1%
- Control and Systems Engineering top 2%
- Co-authors
- Jürgen SchmidhuberKlaus GreffBas R. SteunebrinkRupesh K. SrivastavaFaustino GomezJuergen SchmidhuberMichael WandGiuseppe Cuccu
- Topics
- Neural Networks and Applications (10 papers)Reinforcement Learning in Robotics (7 papers)Evolutionary Algorithms and Applications (4 papers)
- Journals
- IEEE Transactions on Neural Networks and Learning SystemsNeural NetworksResearch Publications (Maastricht University)
- Partner nations
- SwitzerlandCzechiaUnited Kingdom
In The Last Decade
Jan Koutník
18 papers receiving 4.8k citations
Hit Papers
Peers
Comparison fields: 5 of 185
- Artificial Intelligence 2.0k
- Electrical and Electronic Engineering 914
- Computer Vision and Pattern Recognition 869
- Signal Processing 664
- Control and Systems Engineering 502
Countries citing papers authored by Jan Koutník
This map shows the geographic impact of Jan Koutník'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 Jan Koutník with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan Koutník more than expected).
Fields of papers citing papers by Jan Koutník
This network shows the impact of papers produced by Jan Koutník. 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 Jan Koutník. The network helps show where Jan Koutník may publish in the future.
Co-authorship network of co-authors of Jan Koutník
This figure shows the co-authorship network connecting the top 25 collaborators of Jan Koutník. A scholar is included among the top collaborators of Jan Koutník 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 Jan Koutník. Jan Koutník 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 | Accelerating Neural ODEs with Spectral Elements. | 5 |
| 3 | 9 | |
| 4 | Using neural networks to predict the functionality of reconfigurable nano-material networks | 1 |
| 5 | LSTM: A Search Space Odysseybreakdown → | 4435 |
| 6 | 7 | |
| 7 | Unconventional computing using evolution-in-nanomaterio: neural networks meet nanoparticle networks | 2 |
| 8 | 121 | |
| 9 | A Clockwork RNN | 56 |
| 10 | 77 | |
| 11 | 8 | |
| 12 | 66 | |
| 13 | 35 | |
| 14 | 44 | |
| 15 | 7 | |
| 16 | 52 | |
| 17 | 18 | |
| 18 | Proceedings of the 18th international conference on Artificial Neural Networks, Part II | 7 |
| 19 | 0 |
About Jan Koutník
Jan Koutník is a scholar working on Artificial Intelligence, Signal Processing and Statistical and Nonlinear Physics, having authored 19 papers that have together received 5.0k indexed citations. Recurring topics across this work include Neural Networks and Applications (10 papers), Reinforcement Learning in Robotics (7 papers) and Evolutionary Algorithms and Applications (4 papers). The work is most often cited by research in Signal Processing (664 citations), Artificial Intelligence (2.0k citations) and Computer Vision and Pattern Recognition (869 citations). Jan Koutník has collaborated with scholars based in Switzerland, Czechia and United Kingdom. Frequent co-authors include Jürgen Schmidhuber, Klaus Greff, Bas R. Steunebrink, Rupesh K. Srivastava, Faustino Gomez, Juergen Schmidhuber, Michael Wand, Giuseppe Cuccu, Julian Togelius and Miroslav Šnorek. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Neural Networks and Research Publications (Maastricht 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.