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.
Deep learning with convolutional neural networks for EEG decoding and visualization
20172.1k citationsRobin Tibor Schirrmeister, Jost Tobias Springenberg et al.profile →
This map shows the geographic impact of Frank Hutter'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 Frank Hutter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Frank Hutter more than expected).
This network shows the impact of papers produced by Frank Hutter. 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 Frank Hutter. The network helps show where Frank Hutter may publish in the future.
Co-authorship network of co-authors of Frank Hutter
This figure shows the co-authorship network connecting the top 25 collaborators of Frank Hutter.
A scholar is included among the top collaborators of Frank Hutter 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 Frank Hutter. Frank Hutter 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.
Hollmann, Noah, et al.. (2025). Accurate predictions on small data with a tabular foundation model. Nature. 637(8045). 319–326.106 indexed citations breakdown →
Lindauer, Marius & Frank Hutter. (2020). Best Practices for Scientific Research on Neural Architecture Search. Journal of Machine Learning Research. 21(243). 1–18.2 indexed citations
4.
Zela, Arber, Thomas Elsken, Tonmoy Saikia, et al.. (2020). Understanding and Robustifying Differentiable Architecture Search. arXiv (Cornell University).41 indexed citations
Ilg, Eddy, Özgün Çiçek, Aaron Klein, et al.. (2018). Uncertainty Estimates for Optical Flow with Multi-Hypotheses Networks. arXiv (Cornell University).3 indexed citations
7.
Falkner, Stefan, Aaron Klein, & Frank Hutter. (2018). Practical Hyperparameter Optimization for Deep Learning. International Conference on Learning Representations.12 indexed citations
8.
Elsken, Thomas, Jan Hendrik Metzen, & Frank Hutter. (2018). Simple and efficient architecture search for Convolutional Neural Networks. International Conference on Learning Representations.16 indexed citations
9.
Elsken, Thomas, Jan Hendrik Metzen, & Frank Hutter. (2018). Multi-objective Architecture Search for CNNs.. arXiv (Cornell University).10 indexed citations
10.
Klein, Aaron, Stefan Falkner, Jost Tobias Springenberg, & Frank Hutter. (2017). Learning Curve Prediction with Bayesian Neural Networks. International Conference on Learning Representations.67 indexed citations
Hutter, Frank, et al.. (2015). Algorithm Runtime Prediction: Methods & Evaluation (Extended Abstract) .. International Joint Conference on Artificial Intelligence. 4197–4201.3 indexed citations
15.
Hutter, Frank, Balázs Kégl, Rich Caruana, et al.. (2015). Automatic Machine Learning (AutoML). SPIRE - Sciences Po Institutional REpository.4 indexed citations
16.
Wang, Ziyu, Masrour Zoghi, Frank Hutter, David S. Matheson, & Nando de Freitas. (2013). Bayesian optimization in high dimensions via random embeddings. UvA-DARE (University of Amsterdam). 1778–1784.128 indexed citations
17.
Hutter, Frank, Lin Xu, Holger H. Hoos, & Kevin Leyton‐Brown. (2012). Algorithm Runtime Prediction: The State of the Art. arXiv (Cornell University).9 indexed citations
18.
Thornton, Chris, Frank Hutter, Holger H. Hoos, & Kevin Leyton‐Brown. (2012). Auto-WEKA: Automated Selection and Hyper-Parameter Optimization of Classification Algorithms. arXiv (Cornell University).29 indexed citations
19.
Hutter, Frank, Holger H. Hoos, & Thomas Stützle. (2007). Automatic algorithm configuration based on local search. Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles). 1152–1157.138 indexed citations
20.
Hutter, Frank, Youssef Hamadi, Holger H. Hoos, & Kevin Leyton‐Brown. (2006). Performance prediction and automated tuning of randomized and parametric algorithms.17 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.