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.
Kolmogorov's theorem and multilayer neural networks
This map shows the geographic impact of Věra Kůrková'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 Věra Kůrková with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Věra Kůrková more than expected).
This network shows the impact of papers produced by Věra Kůrková. 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 Věra Kůrková. The network helps show where Věra Kůrková may publish in the future.
Co-authorship network of co-authors of Věra Kůrková
This figure shows the co-authorship network connecting the top 25 collaborators of Věra Kůrková.
A scholar is included among the top collaborators of Věra Kůrková 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 Věra Kůrková. Věra Kůrková 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.
Tetko, Igor V., Věra Kůrková, Pavel Karpov, & Fabian J. Theis. (2019). Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II.8 indexed citations
Kůrková, Věra. (1998). Approximation of Functions by Neural Networks.. Natural Computing. 29–35.3 indexed citations
16.
Kárný, Miroslav, Kevin Warwick, & Věra Kůrková. (1998). Dealing with complexity : a neural networks approach. Springer eBooks.6 indexed citations
17.
Kůrková, Věra & Kateřina Hlaváčková‐Schindler. (1994). Approximation of continuous functions by RBF and KBF networks.. The European Symposium on Artificial Neural Networks.2 indexed citations
18.
Kainen, Paul C., et al.. (1994). Uniqueness of network parametrization and faster learning. Neural, Parallel & Scientific Computations archive. 2(4). 459–466.4 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.