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
20164.4k citationsKlaus Greff, Rupesh K. Srivastava et al.IEEE Transactions on Neural Networks and Learning Systemsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Klaus Greff'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 Klaus Greff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Klaus Greff more than expected).
This network shows the impact of papers produced by Klaus Greff. 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 Klaus Greff. The network helps show where Klaus Greff may publish in the future.
Co-authorship network of co-authors of Klaus Greff
This figure shows the co-authorship network connecting the top 25 collaborators of Klaus Greff.
A scholar is included among the top collaborators of Klaus Greff 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 Klaus Greff. Klaus Greff is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
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
9.
Luketina, Jelena, Mathias Berglund, Klaus Greff, & Tapani Raiko. (2016). Scalable gradient-based tuning of continuous regularization hyperparameters. International Conference on Machine Learning. 2952–2960.22 indexed citations
10.
Luketina, Jelena, Mathias Berglund, Klaus Greff, & Tapani Raiko. (2016). 33rd International Conference on Machine Learning, ICML 2016. International Conference on Machine Learning.113 indexed citations
11.
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 →
12.
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
13.
Koutník, Jan, Klaus Greff, Faustino Gomez, & Juergen Schmidhuber. (2014). A Clockwork RNN. International Conference on Machine Learning. 1863–1871.56 indexed citations
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.