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.
'Neural-gas' network for vector quantization and its application to time-series prediction
Countries citing papers authored by Thomas Martinetz
Since
Specialization
Citations
This map shows the geographic impact of Thomas Martinetz'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 Thomas Martinetz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Martinetz more than expected).
Fields of papers citing papers by Thomas Martinetz
This network shows the impact of papers produced by Thomas Martinetz. 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 Thomas Martinetz. The network helps show where Thomas Martinetz may publish in the future.
Co-authorship network of co-authors of Thomas Martinetz
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Martinetz.
A scholar is included among the top collaborators of Thomas Martinetz 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 Thomas Martinetz. Thomas Martinetz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Sieren, Malte Maria, et al.. (2021). An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs. 484–496.5 indexed citations
9.
Hammer, Barbara, Haibo He, & Thomas Martinetz. (2014). Learning and modeling big data. PUB – Publications at Bielefeld University (Bielefeld University).4 indexed citations
Martinetz, Thomas, et al.. (2010). Learning sparse codes for image reconstruction.. The European Symposium on Artificial Neural Networks.3 indexed citations
14.
Barth, Erhardt, et al.. (2008). Learning Data Representations with Sparse Coding Neural Gas. The European Symposium on Artificial Neural Networks. 233–238.8 indexed citations
15.
Udluft, Steffen, et al.. (2007). Neural Rewards Regression for Near-optimal Policy Identification in Markovian and Partial Observable Environments. The European Symposium on Artificial Neural Networks. 301–306.6 indexed citations
16.
Schäfer, Anton Maximilian, et al.. (2007). The Intrinsic Recurrent Support Vector Machine. The European Symposium on Artificial Neural Networks. 325–330.4 indexed citations
Dörr, Michael, Martin Böhme, Thomas Martinetz, & Erhardt Barth. (2005). Predicting, analysing, and guiding eye movements. Neural Information Processing Systems.
19.
Böhme, Martin, et al.. (2004). Saliency Extraction for Gaze-Contingent Displays. GI Jahrestagung (2). 646–650.1 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.