M. Kirby

15.2k total citations
15 papers, 65 citations indexed

About

M. Kirby is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, M. Kirby has authored 15 papers receiving a total of 65 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Computer Networks and Communications and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in M. Kirby's work include Face and Expression Recognition (3 papers), Distributed and Parallel Computing Systems (3 papers) and Advanced Data Storage Technologies (3 papers). M. Kirby is often cited by papers focused on Face and Expression Recognition (3 papers), Distributed and Parallel Computing Systems (3 papers) and Advanced Data Storage Technologies (3 papers). M. Kirby collaborates with scholars based in United States, Italy and Poland. M. Kirby's co-authors include Yui Man Lui, Bruce A. Draper, J. Ross Beveridge, Chris Peterson, F. D. Snider, Y. Oksuzian, M. Trovato, T. J. Phillips, J. Vizán and J. Freeman and has published in prestigious journals such as SHILAP Revista de lepidopterología, Pattern Recognition and Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment.

In The Last Decade

M. Kirby

14 papers receiving 62 citations

Peers

M. Kirby
Z. Hampel-Arias United States
S. Sandilya United States
N. V. Tran United States
P. Harris United States
S. Yang South Korea
P. Zejdl Czechia
H. Zeng China
S. Seddiki Algeria
L. Ren China
Z. Hampel-Arias United States
M. Kirby
Citations per year, relative to M. Kirby M. Kirby (= 1×) peers Z. Hampel-Arias

Countries citing papers authored by M. Kirby

Since Specialization
Citations

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

Fields of papers citing papers by M. Kirby

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Kirby

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

All Works

15 of 15 papers shown
1.
Kirby, M., et al.. (2024). Linear Centroid Encoder for Supervised Principal Component Analysis. Pattern Recognition. 155. 110634–110634. 3 indexed citations
3.
Jamil, Huma, Yajing Liu, Turgay Çağlar, et al.. (2023). Hamming Similarity and Graph Laplacians for Class Partitioning and Adversarial Image Detection. 31. 590–599. 1 indexed citations
4.
Kirby, M., et al.. (2023). Nonlinear feature selection using sparsity-promoted centroid-encoder. Neural Computing and Applications. 35(29). 21883–21902. 4 indexed citations
5.
Kirby, M., et al.. (2023). Correction to: Nonlinear feature selection using sparsity-promoted centroid-encoder. Neural Computing and Applications. 36(1). 521–521. 1 indexed citations
6.
Kirby, M., et al.. (2023). Feature Selection on Big Data using Masked Sparse Bottleneck Centroid-Encoder. 646–655. 2 indexed citations
7.
Herner, K., P. F. Ding, Dave Dykstra, et al.. (2019). Advances and enhancements in the FabrIc for Frontier Experiments project at Fermilab. SHILAP Revista de lepidopterología. 214. 3059–3059. 1 indexed citations
8.
Herner, K., Valerio Benedetto, P. F. Ding, et al.. (2017). Advances in Grid Computing for the Fabric for Frontier Experiments Project at Fermilab. Journal of Physics Conference Series. 898. 52026–52026. 2 indexed citations
9.
Lord, Wesley K., et al.. (2016). Impact of Ultra-High Bypass/ Hybrid Wing Body Integration on Propulsion System Perrformance and Operability (Invited). 54th AIAA Aerospace Sciences Meeting. 3 indexed citations
10.
Fuess, S., O. Gutsche, M. Kirby, et al.. (2015). Fermilab Computing at the Intensity Frontier. Journal of Physics Conference Series. 664(3). 32012–32012. 1 indexed citations
11.
Kirby, M.. (2014). The Fabric for Frontier Experiments Project at Fermilab. Journal of Physics Conference Series. 513(3). 32049–32049. 6 indexed citations
12.
Freeman, J., T. R. Junk, M. Kirby, et al.. (2012). Introduction to HOBIT, a b-jet identification tagger at the CDF experiment optimized for light Higgs boson searches. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 697. 64–76. 14 indexed citations
13.
Lui, Yui Man, J. Ross Beveridge, Bruce A. Draper, & M. Kirby. (2008). Image-set matching using a geodesic distance and cohort normalization. 1–6. 16 indexed citations
14.
Kirby, M., et al.. (2007). Set-to-Set Face Recognition Under Variations in Pose and Illumination. 1–6. 10 indexed citations
15.
Kirby, M.. (1968). Room 706. The Drama Review TDR. 12(3). 141–148. 1 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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