Tibério S. Caetano

2.1k total citations
48 papers, 1.1k citations indexed

About

Tibério S. Caetano is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Tibério S. Caetano has authored 48 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Computer Vision and Pattern Recognition, 22 papers in Artificial Intelligence and 10 papers in Signal Processing. Recurrent topics in Tibério S. Caetano's work include Graph Theory and Algorithms (11 papers), Data Management and Algorithms (9 papers) and Advanced Image and Video Retrieval Techniques (9 papers). Tibério S. Caetano is often cited by papers focused on Graph Theory and Algorithms (11 papers), Data Management and Algorithms (9 papers) and Advanced Image and Video Retrieval Techniques (9 papers). Tibério S. Caetano collaborates with scholars based in Australia, Brazil and United States. Tibério S. Caetano's co-authors include Julian McAuley, Alex Smola, Quoc V. Le, James Petterson, Li Cheng, Novi Quadrianto, Dante Augusto Couto Barone, Terry Caelli, Luciano da Fontoura Costa and Dale Schuurmans and has published in prestigious journals such as Applied Physics Letters, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Image Processing.

In The Last Decade

Tibério S. Caetano

47 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tibério S. Caetano Australia	18	577	540	154	112	69	48	1.1k
Yunhao Yuan China	21	868 1.5×	582 1.1×	103 0.7×	60 0.5×	46 0.7×	103	1.5k
Tamir Hazan Israel	17	534 0.9×	526 1.0×	134 0.9×	99 0.9×	22 0.3×	50	1.2k
Tengyu Ma United States	16	735 1.3×	956 1.8×	64 0.4×	47 0.4×	54 0.8×	45	1.9k
Olivier Breuleux Canada	5	351 0.6×	434 0.8×	137 0.9×	27 0.2×	41 0.6×	5	874
Stephen M. Omohundro United States	13	394 0.7×	504 0.9×	234 1.5×	36 0.3×	53 0.8×	25	1.0k
Dmitry Vetrov Russia	14	684 1.2×	767 1.4×	93 0.6×	40 0.4×	51 0.7×	50	1.5k
Arka Pal United States	2	578 1.0×	681 1.3×	144 0.9×	25 0.2×	54 0.8×	2	1.2k
Irina Higgins United Kingdom	8	621 1.1×	741 1.4×	146 0.9×	26 0.2×	78 1.1×	15	1.4k
Olivier Delalleau Canada	13	935 1.6×	924 1.7×	191 1.2×	30 0.3×	131 1.9×	20	1.8k
Sylvain Gelly France	17	591 1.0×	1.2k 2.3×	98 0.6×	32 0.3×	52 0.8×	46	1.7k

Countries citing papers authored by Tibério S. Caetano

Since Specialization

Citations

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

Fields of papers citing papers by Tibério S. Caetano

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tibério S. Caetano. 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 Tibério S. Caetano. The network helps show where Tibério S. Caetano may publish in the future.

Co-authorship network of co-authors of Tibério S. Caetano

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

All Works

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20 of 20 papers shown

Patrini, Giorgio, Richard Nock, Stephen Hardy, & Tibério S. Caetano. (2016). Fast learning from distributed datasets without entity matching. ANU Open Research (Australian National University). 1909–1917.

Defazio, Aaron & Tibério S. Caetano. (2014). A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation. arXiv (Cornell University). 25. 1250–1258. 9 indexed citations

Charleston, Michael, et al.. (2013). Active subnetwork recovery with a mechanism-dependent scoring function; with application to angiogenesis and organogenesis studies. BMC Bioinformatics. 14(1). 59–59. 5 indexed citations

Petterson, James, et al.. (2012). Learning as MAP Inference in Discrete Graphical Models. ANU Open Research (Australian National University). 25. 1970–1978. 1 indexed citations

Caetano, Tibério S.. (2012). The Interplay of Statistical and Structural Pattern Recognition from a Machine Learning Perspective.. 1 indexed citations

McAuley, Julian & Tibério S. Caetano. (2011). Faster Algorithms for Max-Product Message-Passing. Journal of Machine Learning Research. 12(37). 1349–1388. 12 indexed citations

Petterson, James & Tibério S. Caetano. (2011). Submodular Multi-Label Learning. ANU Open Research (Australian National University). 24. 1512–1520. 34 indexed citations

McAuley, Julian & Tibério S. Caetano. (2010). Exploiting Within-Clique Factorizations in Junction-Tree Algorithms. ANU Open Research (Australian National University). 525–532. 7 indexed citations

Petterson, James, et al.. (2010). Word Features for Latent Dirichlet Allocation. ANU Open Research (Australian National University). 23. 1921–1929. 60 indexed citations

10.

Quadrianto, Novi, et al.. (2010). Multitask Learning without Label Correspondences. Figshare. 23. 1957–1965. 21 indexed citations

11.

Petterson, James & Tibério S. Caetano. (2010). Reverse Multi-Label Learning. ANU Open Research (Australian National University). 23. 1912–1920. 50 indexed citations

12.

Quadrianto, Novi, et al.. (2009). Convex Relaxation of Mixture Regression with Efficient Algorithms. Figshare. 22. 1491–1499. 3 indexed citations

13.

McAuley, Julian & Tibério S. Caetano. (2009). Exact Inference in Graphical Models: is There More to it?. arXiv (Cornell University). 2 indexed citations

14.

Quadrianto, Novi, Alex Smola, Tibério S. Caetano, & Quoc V. Le. (2009). Estimating Labels from Label Proportions. Journal of Machine Learning Research. 10(82). 2349–2374. 84 indexed citations

15.

Caetano, Tibério S., Julian McAuley, Li Cheng, Quoc V. Le, & Alex Smola. (2009). Learning Graph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence. 31(6). 1048–1058. 234 indexed citations

16.

Smola, Alex, Julian McAuley, & Tibério S. Caetano. (2008). Robust Near-Isometric Matching via Structured Learning of Graphical Models. Neural Information Processing Systems. 21. 1057–1064. 2 indexed citations

17.

Ho, Joshua W. K., et al.. (2008). INFERRING DIFFERENTIAL LEUKOCYTE ACTIVITY FROM ANTIBODY MICROARRAYS USING A LATENT VARIABLE MODEL. ANU Open Research (Australian National University). 126–137. 2 indexed citations

18.

Vel, Olivier De, Nianjun Liu, Terry Caelli, & Tibério S. Caetano. (2006). An embedded Bayesian Network Hidden Markov model for digital forensics. Figshare. 1 indexed citations

19.

Caetano, Tibério S., Terry Caelli, Dale Schuurmans, & Dante Augusto Couto Barone. (2006). Graphical Models and Point Pattern Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(10). 1646–1663. 80 indexed citations

20.

Caetano, Tibério S., Sílvia D. Olabarriaga, & Dante Augusto Couto Barone. (2003). Performance evaluation of single and multiple-Gaussian models for skin color modeling. Pure Amsterdam UMC. 275–282. 26 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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