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.
This map shows the geographic impact of Botond Cseke'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 Botond Cseke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Botond Cseke more than expected).
This network shows the impact of papers produced by Botond Cseke. 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 Botond Cseke. The network helps show where Botond Cseke may publish in the future.
Co-authorship network of co-authors of Botond Cseke
This figure shows the co-authorship network connecting the top 25 collaborators of Botond Cseke.
A scholar is included among the top collaborators of Botond Cseke 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 Botond Cseke. Botond Cseke is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cseke, Botond, et al.. (2020). Continual Learning with Bayesian Neural Networks for Non-Stationary Data. International Conference on Learning Representations.16 indexed citations
Cseke, Botond, Manfred Opper, & Guido Sanguinetti. (2013). Approximate inference in latent Gaussian-Markov models from continuous time observations. Edinburgh Research Explorer (University of Edinburgh). 26. 971–979.5 indexed citations
Cseke, Botond & Tom Heskes. (2011). Approximate Marginals in Latent Gaussian Models. Journal of Machine Learning Research. 12(13). 417–454.26 indexed citations
13.
Cseke, Botond & Tom Heskes. (2010). Improving posterior marginal approximations in latent Gaussian models. Data Archiving and Networked Services (DANS). 121–128.6 indexed citations
14.
Heskes, Tom & Botond Cseke. (2009). Discussion of ``Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations'' by H. Rue, S. Martino and N. Chopin. Radboud Repository (Radboud University). 71. 370–370.6 indexed citations
15.
Gerven, Marcel van, Botond Cseke, Robert Oostenveld, & Tom Heskes. (2009). Bayesian Source Localization with the Multivariate Laplace Prior. Radboud Repository (Radboud University). 22. 1901–1909.24 indexed citations
16.
Tsivtsivadze, Evgeni, Botond Cseke, & Tom Heskes. (2009). Kernel Principal Component Ranking: Robust Ranking on Noisy Data. Data Archiving and Networked Services (DANS). 101–113.
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.