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.
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
198411.9k citationsStuart Geman, Donald GemanIEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
Neural Networks and the Bias/Variance Dilemma
19922.3k citationsStuart Geman, Elie Bienenstock et al.Neural Computationprofile →
Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images*
1993689 citationsStuart Geman, Donald GemanJournal of Applied Statisticsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Stuart Geman'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 Stuart Geman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stuart Geman more than expected).
This network shows the impact of papers produced by Stuart Geman. 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 Stuart Geman. The network helps show where Stuart Geman may publish in the future.
Co-authorship network of co-authors of Stuart Geman
This figure shows the co-authorship network connecting the top 25 collaborators of Stuart Geman.
A scholar is included among the top collaborators of Stuart Geman 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 Stuart Geman. Stuart Geman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Geman, Stuart, et al.. (2002). Dynamic programming, tree-width and computation on graphical models.5 indexed citations
9.
Bienenstock, Elie & Stuart Geman. (1998). Compositionality in neural systems. MIT Press eBooks. 223–226.26 indexed citations
10.
Geman, Stuart, et al.. (1998). Dynamic programming algorithms for maximum likelihood decoding.3 indexed citations
11.
Bienenstock, Elie, Stuart Geman, & Daniel Potter. (1996). Compositionality, MDL Priors, and Object Recognition. Neural Information Processing Systems. 9. 838–844.61 indexed citations
12.
Künsch, Hans R., Stuart Geman, & Athanasios Kehagias. (1995). Hidden Markov Random Fields. The Annals of Applied Probability. 5(3).49 indexed citations
Geman, Stuart & Donald Geman. (1993). Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images*. Journal of Applied Statistics. 20(5-6). 25–62.689 indexed citations breakdown →
Geman, Stuart, Elie Bienenstock, & René Doursat. (1992). Neural Networks and the Bias/Variance Dilemma. Neural Computation. 4(1). 1–58.2271 indexed citations breakdown →
17.
Geman, Stuart & Donald Geman. (1984). Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-6(6). 721–741.11851 indexed citations breakdown →
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.