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.
Long Short-Term Memory
199759.5k citationsSepp Hochreiter, Jürgen SchmidhuberNeural Computationprofile →
Countries citing papers authored by Jürgen Schmidhuber
Since
Specialization
Citations
This map shows the geographic impact of Jürgen Schmidhuber'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 Jürgen Schmidhuber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jürgen Schmidhuber more than expected).
Fields of papers citing papers by Jürgen Schmidhuber
This network shows the impact of papers produced by Jürgen Schmidhuber. 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 Jürgen Schmidhuber. The network helps show where Jürgen Schmidhuber may publish in the future.
Co-authorship network of co-authors of Jürgen Schmidhuber
This figure shows the co-authorship network connecting the top 25 collaborators of Jürgen Schmidhuber.
A scholar is included among the top collaborators of Jürgen Schmidhuber 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 Jürgen Schmidhuber. Jürgen Schmidhuber is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Montoya‐Zegarra, Javier A., et al.. (2024). Real World Music Object Recognition. SHILAP Revista de lepidopterología. 7(1). 1–14.2 indexed citations
2.
Ramesh, Aditya, et al.. (2023). Goal-Conditioned Generators of Deep Policies. Proceedings of the AAAI Conference on Artificial Intelligence. 37(6). 7503–7511.2 indexed citations
Irie, Kazuki, et al.. (2021). Going Beyond Linear Transformers with Recurrent Fast Weight Programmers. arXiv (Cornell University).1 indexed citations
5.
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
6.
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 →
7.
Ngo, Hung Q., Matthew Luciw, Ngo Anh Vien, & Jürgen Schmidhuber. (2013). Upper confidence weighted learning for efficient exploration in multiclass prediction with binary feedback. Research Portal (Queen's University Belfast). 2488–2494.3 indexed citations
8.
Leitner, Jürgen, et al.. (2013). ALife in humanoids: Developing a framework to employ artificial life techniques for high-level perception and cognition tasks on humanoid robots. QUT ePrints (Queensland University of Technology).1 indexed citations
9.
Liwicki, Marcus, Alex Graves, Horst Bunke, & Jürgen Schmidhuber. (2007). A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks. Bern Open Repository and Information System (University of Bern).123 indexed citations
10.
Förster, Alexander, Alex Graves, & Jürgen Schmidhuber. (2007). RNN-based Learning of Compact Maps for Efficient Robot Localization. mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich). 537–542.8 indexed citations
Gagliolo, Matteo & Jürgen Schmidhuber. (2006). Dynamic Algorithm Portfolios. Annals of Mathematics and Artificial Intelligence. 47.17 indexed citations
13.
Graves, Alex & Jürgen Schmidhuber. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks. 18(5-6). 602–610.3634 indexed citations breakdown →
14.
Graves, Alex & Jürgen Schmidhuber. (2005). Framewise phoneme classification with bidirectional lstm and other neural network architectures.64 indexed citations
15.
Graves, Alex, et al.. (2004). A Comparison Between Spiking and Differentiable Recurrent Neural Networks on Spoken Digit Recognition. mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich). 164–168.5 indexed citations
16.
Eck, Douglas & Jürgen Schmidhuber. (2002). Learning the Long-Term Structure of the Blues.1 indexed citations
17.
Sałustowicz, Rafał & Jürgen Schmidhuber. (1999). From probabilities to programs with probabilistic incremental program evolution. 433–450.1 indexed citations
18.
Hochreiter, Sepp & Jürgen Schmidhuber. (1997). Long Short-Term Memory. Neural Computation. 9(8). 1735–1780.59500 indexed citations breakdown →
19.
Hochreiter, Sepp & Jürgen Schmidhuber. (1996). LSTM can Solve Hard Long Time Lag Problems. Neural Information Processing Systems. 9. 473–479.481 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.