Luís M. Silva

504 total citations
18 papers, 271 citations indexed

About

Luís M. Silva is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Luís M. Silva has authored 18 papers receiving a total of 271 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 6 papers in Signal Processing. Recurrent topics in Luís M. Silva's work include Neural Networks and Applications (8 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and Blind Source Separation Techniques (3 papers). Luís M. Silva is often cited by papers focused on Neural Networks and Applications (8 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and Blind Source Separation Techniques (3 papers). Luís M. Silva collaborates with scholars based in Portugal, Brazil and Italy. Luís M. Silva's co-authors include Luı́s A. Alexandre, Jorge M. Santos, Jaime S. Cardoso, Joaquim P. Marques de Sá, Nelson Barros, Tânia Fontes, Ana Cristina Carvalho, Ricardo Sousa, F. Vaz and António Teixeira and has published in prestigious journals such as The Science of The Total Environment, Neural Computation and Neural Networks.

In The Last Decade

Luís M. Silva

16 papers receiving 258 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Luís M. Silva Portugal 10 121 56 51 42 32 18 271
Yongchan Kwon South Korea 8 168 1.4× 59 1.1× 41 0.8× 13 0.3× 11 0.3× 12 462
Edwin Vans Fiji 5 114 0.9× 50 0.9× 25 0.5× 23 0.5× 19 0.6× 6 358
Zhang Xuegong China 3 83 0.7× 55 1.0× 124 2.4× 18 0.4× 12 0.4× 6 361
Ting Xu China 13 125 1.0× 102 1.8× 23 0.5× 11 0.3× 41 1.3× 34 390
Óscar Reyes Spain 12 342 2.8× 130 2.3× 30 0.6× 19 0.5× 11 0.3× 20 541
Di Yang China 12 109 0.9× 103 1.8× 33 0.6× 25 0.6× 73 2.3× 53 438
Norbert Jankowski Poland 9 272 2.2× 76 1.4× 54 1.1× 31 0.7× 6 0.2× 30 429
Chao Yuan China 11 147 1.2× 94 1.7× 63 1.2× 35 0.8× 7 0.2× 51 329
Francisco J. Moreno-Barea Spain 3 103 0.9× 75 1.3× 14 0.3× 18 0.4× 13 0.4× 4 278

Countries citing papers authored by Luís M. Silva

Since Specialization
Citations

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

Fields of papers citing papers by Luís M. Silva

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Luís M. Silva. 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 Luís M. Silva. The network helps show where Luís M. Silva may publish in the future.

Co-authorship network of co-authors of Luís M. Silva

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

All Works

18 of 18 papers shown
1.
Ramos, Daniel, et al.. (2025). Predicting Red Blood Cell Transfusion in Elective Cardiac Surgery: A Machine Learning Approach. Mathematical and Computational Applications. 30(2). 22–22. 1 indexed citations
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Silva, Luís M., et al.. (2016). High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning. SLAS DISCOVERY. 21(3). 252–259. 50 indexed citations
6.
Silva, Luís M., et al.. (2016). Multi-source deep transfer learning for cross-sensor biometrics. Neural Computing and Applications. 28(9). 2461–2475. 24 indexed citations
7.
Fontes, Tânia, et al.. (2014). Can artificial neural networks be used to predict the origin of ozone episodes?. The Science of The Total Environment. 488-489. 197–207. 27 indexed citations
8.
Silva, Luís M., et al.. (2014). Improving transfer learning accuracy by reusing Stacked Denoising Autoencoders. 1380–1387. 24 indexed citations
9.
Silva, Luís M., et al.. (2014). CLASSIFICATION PERFORMANCE OF MULTILAYER PERCEPTRONS WITH DIFFERENT RISK FUNCTIONALS. International Journal of Pattern Recognition and Artificial Intelligence. 28(6). 1450013–1450013. 1 indexed citations
10.
Silva, Luís M., et al.. (2014). Improving Performance on Problems with Few Labelled Data by Reusing Stacked Auto-Encoders. 367–372. 4 indexed citations
11.
Silva, Luís M., et al.. (2013). Using Different Cost Functions to Train Stacked Auto-Encoders. 114–120. 28 indexed citations
12.
Sá, Joaquim P. Marques de, Luís M. Silva, Jorge M. Santos, & Luı́s A. Alexandre. (2012). Minimum Error Entropy Classification. Studies in computational intelligence. 23 indexed citations
13.
Silva, Luís M., et al.. (2010). The MEE Principle in Data Classification: A Perceptron-Based Analysis. Neural Computation. 22(10). 2698–2728. 11 indexed citations
14.
Silva, Luís M., et al.. (2008). Data classification with multilayer perceptrons using a generalized error function. Neural Networks. 21(9). 1302–1310. 35 indexed citations
15.
Silva, Luís M., et al.. (2006). Error Entropy in Classification Problems: A Univariate Data Analysis. Neural Computation. 18(9). 2036–2061. 12 indexed citations
16.
Silva, Luís M., et al.. (2006). New developments of the Z-EDM algorithm. 3686. 1067–1072. 4 indexed citations
17.
Silva, Luís M., et al.. (2005). Neural Network Classification using Shannon's Entropy. The European Symposium on Artificial Neural Networks. 217–222. 21 indexed citations
18.
Teixeira, António, et al.. (2004). SAPWindows - towards a versatile modular articulatory synthesizer. 31–34. 5 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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