Tibério S. Caetano

47 papers receiving 1.1k citations

Peers

Tibério S. Caetano
Comparison fields: 5 of 110
  • Computer Vision and Pattern Recognition 577
  • Artificial Intelligence 540
  • Signal Processing 154
  • Aerospace Engineering 112
  • Statistical and Nonlinear Physics 69
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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

20 of 20 papers shown
#WorkIndexed citations
1
Fast learning from distributed datasets without entity matching
0
2 9
3 5
4
The Interplay of Statistical and Structural Pattern Recognition from a Machine Learning Perspective.
1
5
Learning as MAP Inference in Discrete Graphical Models
1
6
Submodular Multi-Label Learning
34
7 12
8
Multitask Learning without Label Correspondences
21
9
Reverse Multi-Label Learning
50
10
Exploiting Within-Clique Factorizations in Junction-Tree Algorithms
7
11
Word Features for Latent Dirichlet Allocation
60
12
Exact Inference in Graphical Models: is There More to it?
2
13 84
14
Convex Relaxation of Mixture Regression with Efficient Algorithms
3
15 5
16 234
17
Robust Near-Isometric Matching via Structured Learning of Graphical Models
2
18
An embedded Bayesian Network Hidden Markov model for digital forensics
1
19 80
20 26

About Tibério S. Caetano

Tibério S. Caetano is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence, having authored 48 papers that have together received 1.1k indexed citations. Recurring topics across this work include Graph Theory and Algorithms (11 papers), Data Management and Algorithms (9 papers) and Advanced Image and Video Retrieval Techniques (9 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (577 citations), Artificial Intelligence (540 citations) and Signal Processing (154 citations). Tibério S. Caetano has collaborated with scholars based in Australia, Brazil and United States. Frequent 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. Their work appears in journals such as Applied Physics Letters, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Image Processing.

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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