Jan Koutník

8.0k total citations · 1 hit paper
19 papers, 5.0k citations indexed

About

Jan Koutník is a scholar working on Artificial Intelligence, Control and Systems Engineering and Electrical and Electronic Engineering. According to data from OpenAlex, Jan Koutník has authored 19 papers receiving a total of 5.0k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 4 papers in Control and Systems Engineering and 4 papers in Electrical and Electronic Engineering. Recurrent topics in Jan Koutník's work include Neural Networks and Applications (10 papers), Reinforcement Learning in Robotics (7 papers) and Evolutionary Algorithms and Applications (4 papers). Jan Koutník is often cited by papers focused on Neural Networks and Applications (10 papers), Reinforcement Learning in Robotics (7 papers) and Evolutionary Algorithms and Applications (4 papers). Jan Koutník collaborates with scholars based in Switzerland, Czechia and United Kingdom. Jan Koutník's 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 and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, Neural Networks and Research Publications (Maastricht University).

In The Last Decade

Jan Koutník

18 papers receiving 4.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

LSTM: A Search Space Odyssey

2016 4.4k citations Klaus Greff, Rupesh K. Srivastava et al. IEEE Transactions on Neural Networks and Learning Systems profile →

Peers

Jan Koutník

AI

EEE

CVPR

SP

CSE

Klaus Greff United States

Bas R. Steunebrink Netherlands

Felix A. Gers Switzerland

Fred Cummins Ireland

Shanghang Zhang China

Abdulkadir Şengür Türkiye

Chai Quek Singapore

Amaury Lendasse Finland

Jianxin Li China

Lipo Wang Singapore

Klaus Greff United States

Jan Koutník

1.8k ×0.9

AI

873 ×1.0

EEE

893 ×1.0

CVPR

555 ×0.8

SP

478 ×1.0

CSE

Citations per year, relative to Jan Koutník Jan Koutník (= 1×) peers Klaus Greff

Countries citing papers authored by Jan Koutník

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Sort: Min cites: Since: Top N: Style:

19 of 19 papers shown

1.

Masci, Jonathan, et al.. (2020). Infinite-Horizon Differentiable Model Predictive Control. arXiv (Cornell University). 1 indexed citations

2.

Masci, Jonathan, et al.. (2019). Accelerating Neural ODEs with Spectral Elements.. arXiv (Cornell University). 5 indexed citations

3.

Masci, Jonathan, et al.. (2019). SNODE: Spectral Discretization of Neural ODEs for System Identification. arXiv (Cornell University). 9 indexed citations

4.

Greff, Klaus, Rudolf M.J. van Damme, Jan Koutník, et al.. (2017). Using neural networks to predict the functionality of reconfigurable nano-material networks. University of Twente Research Information. 9. 339–351. 1 indexed citations

5.

Greff, Klaus, Rupesh K. Srivastava, Jan Koutník, Bas R. Steunebrink, & Jürgen Schmidhuber. (2016). LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems. 28(10). 2222–2232. 4435 indexed citations breakdown →

6.

Steenkiste, Sjoerd van, Jan Koutník, Kurt Driessens, & Jürgen Schmidhuber. (2016). A Wavelet-based Encoding for Neuroevolution. Research Publications (Maastricht University). 6. 517–524. 7 indexed citations

7.

Greff, Klaus, Rudolf M.J. van Damme, Jan Koutník, et al.. (2016). Unconventional computing using evolution-in-nanomaterio: neural networks meet nanoparticle networks. University of Twente Research Information. 15–20. 2 indexed citations

8.

Wand, Michael, Jan Koutník, & Jürgen Schmidhuber. (2016). Lipreading with long short-term memory. 6115–6119. 121 indexed citations

9.

Koutník, Jan, Klaus Greff, Faustino Gomez, & Juergen Schmidhuber. (2014). A Clockwork RNN. International Conference on Machine Learning. 1863–1871. 56 indexed citations

10.

Koutník, Jan, Juergen Schmidhuber, & Faustino Gomez. (2014). Evolving deep unsupervised convolutional networks for vision-based reinforcement learning. 541–548. 77 indexed citations

11.

Koutník, Jan, Giuseppe Cuccu, Jürgen Schmidhuber, & Faustino Gomez. (2013). Evolving Large-Scale Neural Networks for Vision-Based TORCS. SUPSI ARIS. 206–212. 8 indexed citations

12.

Koutník, Jan, Giuseppe Cuccu, Jürgen Schmidhuber, & Faustino Gomez. (2013). Evolving large-scale neural networks for vision-based reinforcement learning. 1061–1068. 66 indexed citations

13.

Kordík, Pavel, et al.. (2010). Meta-learning approach to neural network optimization. Neural Networks. 23(4). 568–582. 35 indexed citations

14.

Koutník, Jan, Faustino Gomez, & Jürgen Schmidhuber. (2010). Evolving neural networks in compressed weight space. Zenodo (CERN European Organization for Nuclear Research). 619–626. 44 indexed citations

15.

Koutník, Jan, Faustino Gomez, & Jürgen Schmidhuber. (2010). Searching for Minimal Neural Networks in Fourier Space. 7 indexed citations

16.

Togelius, Julian, et al.. (2009). Super mario evolution. IT University Of Copenhagen (IT University of Copenhagen). 156–161. 52 indexed citations

17.

Koutník, Jan, et al.. (2009). HyperNEAT controlled robots learn how to drive on roads in simulated environment. 1087–1092. 18 indexed citations

18.

Kůrková, Věra, Roman Neruda, & Jan Koutník. (2008). Proceedings of the 18th international conference on Artificial Neural Networks, Part II. 7 indexed citations

19.

Koutník, Jan & Miroslav Šnorek. (2004). Enhancement of categorizing and learning module (CALM) - embedded defection of signal change. 4. 3233–3237.

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.

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