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.
Countries citing papers authored by Michael U. Gutmann
Since
Specialization
Citations
This map shows the geographic impact of Michael U. Gutmann'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 Michael U. Gutmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael U. Gutmann more than expected).
Fields of papers citing papers by Michael U. Gutmann
This network shows the impact of papers produced by Michael U. Gutmann. 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 Michael U. Gutmann. The network helps show where Michael U. Gutmann may publish in the future.
Co-authorship network of co-authors of Michael U. Gutmann
This figure shows the co-authorship network connecting the top 25 collaborators of Michael U. Gutmann.
A scholar is included among the top collaborators of Michael U. Gutmann 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 Michael U. Gutmann. Michael U. Gutmann is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bramley, Neil R, et al.. (2021). Bayesian Experimental Design for Intractable Models of Cognition. eScholarship (California Digital Library). 43(43).1 indexed citations
4.
Järvenpää, Marko, Michael U. Gutmann, Aki Vehtari, & Pekka Marttinen. (2019). Parallel Gaussian process surrogate method to accelerate likelihood-free inference. arXiv (Cornell University).1 indexed citations
Srivastava, Akash, Lazar Valkov, Chris Russell, Michael U. Gutmann, & Charles Sutton. (2017). VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning. Edinburgh Research Explorer (University of Edinburgh). 30. 3308–3318.96 indexed citations
7.
Gutmann, Michael U., Ritabrata Dutta, Samuel Kaski, & Jukka Corander. (2017). Likelihood-free inference via classification. Statistics and Computing. 28(2). 411–425.37 indexed citations
8.
Vuollekoski, Henri, Marko Järvenpää, Pekka Marttinen, et al.. (2017). ELFI: Engine for Likelihood-Free Inference. Journal of Machine Learning Research. 19(1). 643–649.10 indexed citations
Dutta, Ritabrata, Jukka Corander, Samuel Kaski, & Michael U. Gutmann. (2016). Likelihood-free inference by penalised logistic regression. arXiv (Cornell University).4 indexed citations
Sasaki, Hiroaki, Hayaru Shouno, Michael U. Gutmann, & Aapo Hyvärinen. (2014). Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations. Journal of Machine Learning Research. 33. 868–876.1 indexed citations
14.
Gutmann, Michael U. & Aapo Hyvärinen. (2012). Learning a selectivity--invariance--selectivity feature extraction architecture for images. Edinburgh Research Explorer (University of Edinburgh).2 indexed citations
Sasaki, Hiroaki, Michael U. Gutmann, Hayaru Shouno, & Aapo Hyvärinen. (2012). Topographic Analysis of Correlated Components. Edinburgh Research Explorer (University of Edinburgh). 365–378.1 indexed citations
Gutmann, Michael U. & Aapo Hyvärinen. (2009). Learning reconstruction and prediction of natural stimuli by a population of spiking neurons. Edinburgh Research Explorer (University of Edinburgh).
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.