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.
Object categorization by learned universal visual dictionary
2005607 citationsJohn Winn, Antonio Criminisi et al.profile →
Citations per year, relative to Thomas P. Minka Thomas P. Minka (= 1×)
peers
Suvrit Sra
Countries citing papers authored by Thomas P. Minka
Since
Specialization
Citations
This map shows the geographic impact of Thomas P. Minka'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 P. Minka with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas P. Minka more than expected).
This network shows the impact of papers produced by Thomas P. Minka. 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 P. Minka. The network helps show where Thomas P. Minka may publish in the future.
Co-authorship network of co-authors of Thomas P. Minka
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas P. Minka.
A scholar is included among the top collaborators of Thomas P. Minka 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 P. Minka. Thomas P. Minka is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhang, Cheng, et al.. (2015). GroupBox: A generative model for group recommendation. KTH Publication Database DiVA (KTH Royal Institute of Technology).3 indexed citations
4.
Qi, Yuan, et al.. (2010). Sparse-posterior Gaussian processes for general likelihoods. Uncertainty in Artificial Intelligence. 450–457.11 indexed citations
Minka, Thomas P.. (2005). Divergence measures and message passing. 17.256 indexed citations
7.
Winn, John, Antonio Criminisi, & Thomas P. Minka. (2005). Object categorization by learned universal visual dictionary. 1800–1807 Vol. 2.607 indexed citations breakdown →
8.
Minka, Thomas P.. (2004). A comparison of numerical optimizers for logistic regression.172 indexed citations
9.
Minka, Thomas P.. (2004). Exemplar-based Likelihoods Using the PDF Projection Theorem. 3.5 indexed citations
10.
Minka, Thomas P.. (2003). The ‘summation hack’ as an outlier model.14 indexed citations
11.
Minka, Thomas P.. (2003). Bayesian inference, entropy, and the multinomial distribution.24 indexed citations
12.
Minka, Thomas P. & John Lafferty. (2002). Expectation-propagation for the generative aspect model. Uncertainty in Artificial Intelligence. 352–359.266 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.