Florence Forbes

11.0k total citations · 2 hit papers
93 papers, 2.7k citations indexed

About

Florence Forbes is a scholar working on Artificial Intelligence, Statistics and Probability and Computer Vision and Pattern Recognition. According to data from OpenAlex, Florence Forbes has authored 93 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Artificial Intelligence, 31 papers in Statistics and Probability and 18 papers in Computer Vision and Pattern Recognition. Recurrent topics in Florence Forbes's work include Bayesian Methods and Mixture Models (34 papers), Statistical Methods and Inference (19 papers) and Medical Image Segmentation Techniques (11 papers). Florence Forbes is often cited by papers focused on Bayesian Methods and Mixture Models (34 papers), Statistical Methods and Inference (19 papers) and Medical Image Segmentation Techniques (11 papers). Florence Forbes collaborates with scholars based in France, Australia and United States. Florence Forbes's co-authors include Gilles Celeux, Olivier François, D. M. Titterington, Caroline Robert, Chibiao Chen, Éric Durand, Nathalie Peyrard, Michel Dojat, Radu Horaud and Darren Wraith and has published in prestigious journals such as Journal of the American Statistical Association, Bioinformatics and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Florence Forbes

87 papers receiving 2.6k citations

Hit Papers

Deviance information criteria for missing data models 2006 2026 2012 2019 2006 2007 200 400 600
What are hit papers?

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.

  1. Deviance information criteria for missing data models
    2006 629 citations Florence Forbes et al. profile →
  2. Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study
    2007 603 citations Florence Forbes et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Florence Forbes France 19 782 568 545 538 345 93 2.7k
C. A. Glasbey United Kingdom 24 313 0.4× 217 0.4× 560 1.0× 147 0.3× 268 0.8× 119 3.2k
K. E. Basford Australia 29 1.6k 2.0× 742 1.3× 437 0.8× 783 1.5× 76 0.2× 134 5.0k
Xiuwen Liu United States 33 588 0.8× 583 1.0× 1.2k 2.2× 73 0.1× 265 0.8× 180 5.8k
Ian L. Dryden United Kingdom 27 368 0.5× 186 0.3× 399 0.7× 202 0.4× 91 0.3× 109 2.4k
Idris A. Eckley United Kingdom 14 470 0.6× 103 0.2× 186 0.3× 355 0.7× 306 0.9× 62 3.0k
Vladimir Makarenkov Canada 31 1.3k 1.6× 275 0.5× 427 0.8× 53 0.1× 167 0.5× 83 4.0k
Dirk Husmeier United Kingdom 28 747 1.0× 400 0.7× 193 0.4× 145 0.3× 287 0.8× 131 3.6k
Washington Mio United States 16 196 0.3× 380 0.7× 987 1.8× 60 0.1× 195 0.6× 69 3.1k
Mairi Clarke United Kingdom 22 1.0k 1.3× 92 0.2× 827 1.5× 135 0.3× 225 0.7× 44 4.4k
Arnoldo Frigessi Norway 30 501 0.6× 410 0.7× 73 0.1× 732 1.4× 121 0.4× 121 4.5k

Countries citing papers authored by Florence Forbes

Since Specialization
Citations

This map shows the geographic impact of Florence Forbes'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 Florence Forbes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Florence Forbes more than expected).

Fields of papers citing papers by Florence Forbes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Florence Forbes. 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 Florence Forbes. The network helps show where Florence Forbes may publish in the future.

Co-authorship network of co-authors of Florence Forbes

This figure shows the co-authorship network connecting the top 25 collaborators of Florence Forbes. A scholar is included among the top collaborators of Florence Forbes 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 Florence Forbes. Florence Forbes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Forbes, Florence, et al.. (2024). Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis. Artificial Intelligence in Medicine. 150. 102830–102830. 63 indexed citations
2.
Dojat, Michel, et al.. (2023). Brain Subtle Anomaly Detection Based on Auto-Encoders Latent Space Analysis: Application To De Novo Parkinson Patients. arXiv (Cornell University). 12. 1–5. 1 indexed citations
3.
Nguyen, Hien D., Sharon Lee, & Florence Forbes. (2022). A Festschrift for Geoff McLachlan. Australian & New Zealand Journal of Statistics. 64(2). 111–116. 1 indexed citations
4.
Nguyen, Hien D. & Florence Forbes. (2022). Global implicit function theorems and the online expectation–maximisation algorithm. Australian & New Zealand Journal of Statistics. 64(2). 255–281. 2 indexed citations
5.
Nguyen, Hien D., Florence Forbes, & Geoffrey J. McLachlan. (2020). Mini-batch learning of exponential family finite mixture models. Statistics and Computing. 30(4). 731–748. 12 indexed citations
6.
Nguyen, Hien D., Julyan Arbel, Hongliang Lü, & Florence Forbes. (2020). Approximate Bayesian Computation Via the Energy Statistic. IEEE Access. 8. 131683–131698.
7.
Nguyen, Hien D., Faïcel Chamroukhi, & Florence Forbes. (2019). Approximation results regarding the multiple-output Gaussian gated mixture of linear experts model. Neurocomputing. 366. 208–214. 10 indexed citations
8.
Forbes, Florence, et al.. (2019). No Structural Differences Are Revealed by VBM in ‘De Novo’ Parkinsonian Patients. Studies in health technology and informatics. 264. 268–272. 3 indexed citations
9.
Zheng, Fei, Florence Forbes, Stéphane Bonnet, et al.. (2019). Characterization of Daily Glycemic Variability in Subjects with Type 1 Diabetes Using a Mixture of Metrics. Diabetes Technology & Therapeutics. 22(4). 301–313. 1 indexed citations
10.
Wojtusciszyn, Anne, Florence Forbes, Stéphane Bonnet, et al.. (2019). Glycemic variability indices can be used to diagnose islet transplantation success in type 1 diabetic patients. Acta Diabetologica. 57(3). 335–345. 3 indexed citations
11.
Deleforge, Antoine & Florence Forbes. (2016). Rectified binaural ratio: A complex T-distributed feature for robust\n sound localization. arXiv (Cornell University). 1 indexed citations
12.
Gebru, Israel D., Xavier Alameda-Pineda, Florence Forbes, & Radu Horaud. (2016). EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 38(12). 2402–2415. 62 indexed citations
13.
Girard, Stéphane, et al.. (2015). A class of multivariate copulas based on products of bivariate copulas. Journal of Multivariate Analysis. 140. 363–376. 21 indexed citations
14.
Vincent, Thomas, Solveig Badillo, Laurent Risser, et al.. (2014). Flexible multivariate hemodynamics fMRI data analyses and simulations with PyHRF. Frontiers in Neuroscience. 8. 67–67. 9 indexed citations
15.
Chaâri, Lotfi, Florence Forbes, Thomas Vincent, & Philippe Ciuciu. (2012). Hemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework. Lecture notes in computer science. 15(Pt 3). 180–188. 12 indexed citations
16.
Scherrer, Benoît, Florence Forbes, Catherine Garbay, & Michel Dojat. (2009). Distributed Local MRF Models for Tissue and Structure Brain Segmentation. IEEE Transactions on Medical Imaging. 28(8). 1278–1295. 52 indexed citations
17.
Blanchet, Juliette & Florence Forbes. (2008). Triplet Markov Fields for the Classification of Complex Structure Data. IEEE Transactions on Pattern Analysis and Machine Intelligence. 30(6). 1055–1067. 11 indexed citations
18.
Blanchet, Juliette, Florence Forbes, & Cordelia Schmid. (2005). Markov Random Fields for Recognizing textures modeled by Feature Vectors. HAL (Le Centre pour la Communication Scientifique Directe). 159(1). 60–8. 1 indexed citations
19.
Forbes, Florence & Adrian E. Raftery. (1999). Bayesian Morphology: Fast Unsupervised Bayesian Image Analysis. Journal of the American Statistical Association. 94(446). 555–568. 7 indexed citations
20.
Forbes, Florence & Bernard Ycart. (1998). Counting stable sets on Cartesian products of graphs. Discrete Mathematics. 186(1-3). 105–116. 3 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.

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