V. Gaitan

3.6k total citations
12 papers, 66 citations indexed

About

V. Gaitan is a scholar working on Artificial Intelligence, Computational Mechanics and Aerospace Engineering. According to data from OpenAlex, V. Gaitan has authored 12 papers receiving a total of 66 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 3 papers in Computational Mechanics and 3 papers in Aerospace Engineering. Recurrent topics in V. Gaitan's work include Neural Networks and Applications (6 papers), Astronomical Observations and Instrumentation (3 papers) and Particle physics theoretical and experimental studies (2 papers). V. Gaitan is often cited by papers focused on Neural Networks and Applications (6 papers), Astronomical Observations and Instrumentation (3 papers) and Particle physics theoretical and experimental studies (2 papers). V. Gaitan collaborates with scholars based in Spain and Switzerland. V. Gaitan's co-authors include L. Garrido, M. Serra‐Ricart, Xavier Calbet, J. Licandro, E. Joven, F. Sánchez, T. Lux, Sergio Gómez, A. Aparicio and J. E. Beckman and has published in prestigious journals such as The Astrophysical Journal, Computer Physics Communications and The Astronomical Journal.

In The Last Decade

V. Gaitan

12 papers receiving 59 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
V. Gaitan Spain 5 26 22 11 11 10 12 66
C. Davis United States 3 41 1.6× 11 0.5× 15 1.4× 4 0.4× 26 2.6× 8 68
I. Reyes Chile 4 64 2.5× 21 1.0× 15 1.4× 13 1.2× 17 1.7× 10 110
A. Volpicelli Italy 5 29 1.1× 11 0.5× 3 0.3× 11 1.0× 7 0.7× 17 97
N. Kuropatkin United States 3 58 2.2× 10 0.5× 29 2.6× 8 0.7× 23 2.3× 6 90
S. Farrens France 6 35 1.3× 7 0.3× 13 1.2× 4 0.4× 18 1.8× 7 65
Sara Webb Australia 5 59 2.3× 8 0.4× 4 0.4× 17 1.5× 9 0.9× 9 80
Christopher J. Shallue United States 4 47 1.8× 21 1.0× 2 0.2× 5 0.5× 22 2.2× 6 71
T. Prince India 5 43 1.7× 13 0.6× 6 0.5× 16 1.5× 1 0.1× 15 76
M. Rigault France 7 72 2.8× 29 1.3× 2 0.2× 25 2.3× 16 1.6× 14 107
Luca Tortorelli Germany 7 100 3.8× 9 0.4× 10 0.9× 4 0.4× 66 6.6× 18 120

Countries citing papers authored by V. Gaitan

Since Specialization
Citations

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

Fields of papers citing papers by V. Gaitan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of V. Gaitan

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

All Works

12 of 12 papers shown
1.
Licandro, J., et al.. (2023). Scientific CMOS Sensors in Astronomy: IMX455 and IMX411. Publications of the Astronomical Society of the Pacific. 135(1047). 55001–55001. 16 indexed citations
2.
Sánchez, F., et al.. (2020). Likelihood-free inference of experimental neutrino oscillations using neural spline flows. Physical review. D. 101(11). 1 indexed citations
3.
Sánchez, F., et al.. (2020). Exhaustive neural importance sampling applied to Monte Carlo event generation. Physical review. D. 102(1). 8 indexed citations
4.
Garrido, L., Sergio Gómez, V. Gaitan, & M. Serra‐Ricart. (1996). A REGULARIZATION TERM TO AVOID THE SATURATION OF THE SIGMOIDS IN MULTILAYER NEURAL NETWORKS. International Journal of Neural Systems. 7(3). 257–262. 4 indexed citations
5.
Serra‐Ricart, M., A. Aparicio, L. Garrido, & V. Gaitan. (1996). A New Method Based on Artificial Neural Network Techniques for Determining the Fraction of Binaries in Star Clusters. The Astrophysical Journal. 462. 221–221. 4 indexed citations
6.
Serra‐Ricart, M., et al.. (1995). Multidimensional interpolation using artificial neural networks: Application to an H I cloud in Perseus. The Astronomical Journal. 109. 312–312. 1 indexed citations
7.
Garrido, L., V. Gaitan, M. Serra‐Ricart, & Xavier Calbet. (1995). USE OF MULTILAYER FEEDFORWARD NEURAL NETS AS A DISPLAY METHOD FOR MULTIDIMENSIONAL DISTRIBUTIONS. International Journal of Neural Systems. 6(3). 273–282. 5 indexed citations
8.
Serra‐Ricart, M., L. Garrido, & V. Gaitan. (1994). Statistical methods in astronomy based on artificial neural network techniques. Vistas in Astronomy. 38. 257–263. 1 indexed citations
9.
Garrido, L., et al.. (1994). Test of agreement between two multidimensional empirical distributions. Computer Physics Communications. 84(1-3). 297–306. 1 indexed citations
10.
Serra‐Ricart, M., Xavier Calbet, L. Garrido, & V. Gaitan. (1993). Multidimensional statistical analysis using artificial neural networks - Astronomical applications. The Astronomical Journal. 106. 1685–1685. 18 indexed citations
11.
Serra‐Ricart, M., et al.. (1992). Faint object classification using neural networks. 25. 254. 1 indexed citations
12.
Garrido, L. & V. Gaitan. (1991). USE OF NEURAL NETS TO MEASURE THE τ POLARIZATION AND ITS BAYESIAN INTERPRETATION. International Journal of Neural Systems. 2(3). 221–228. 6 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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