J. R. Hoskins

1000 total citations
2 papers, 12 citations indexed

About

J. R. Hoskins is a scholar working on Computer Vision and Pattern Recognition, Atomic and Molecular Physics, and Optics and Artificial Intelligence. According to data from OpenAlex, J. R. Hoskins has authored 2 papers receiving a total of 12 indexed citations (citations by other indexed papers that have themselves been cited), including 1 paper in Computer Vision and Pattern Recognition, 1 paper in Atomic and Molecular Physics, and Optics and 1 paper in Artificial Intelligence. Recurrent topics in J. R. Hoskins's work include Advanced Neural Network Applications (1 paper), Domain Adaptation and Few-Shot Learning (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). J. R. Hoskins is often cited by papers focused on Advanced Neural Network Applications (1 paper), Domain Adaptation and Few-Shot Learning (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). J. R. Hoskins collaborates with scholars based in United States. J. R. Hoskins's co-authors include L. P. Alonzi, Simonetta Liuti, Jake Grigsby, Matthias Burkardt and Kevin J. Roberts and has published in prestigious journals such as Physical review. D and Murray State's Digital Commons (Murray State University).

In The Last Decade

J. R. Hoskins

2 papers receiving 12 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
J. R. Hoskins United States	2	10	1	1	1	1	2	12
D. Navas-Nicolás France	2	10 1.0×				1 1.0×	2	10
S. Akar Netherlands	2	10 1.0×					2	10
I. Vorobyev Germany	2	10 1.0×		1 1.0×			4	11
Z. Ajaltouni Netherlands	2	11 1.1×					2	11
M. Adinolfi Netherlands	2	11 1.1×					2	11
B. Adeva Netherlands	2	11 1.1×					2	11
M. Adinolfi France	3	11 1.1×					3	11
A. Merzlaya Russia	3	11 1.1×		1 1.0×			6	12
A. Dobrin Sweden	2	11 1.1×					6	11
S. Puławski Poland	3	10 1.0×	1 1.0×				7	11

Countries citing papers authored by J. R. Hoskins

Since Specialization

Citations

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

Fields of papers citing papers by J. R. Hoskins

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J. R. Hoskins. 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 J. R. Hoskins. The network helps show where J. R. Hoskins may publish in the future.

Co-authorship network of co-authors of J. R. Hoskins

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

All Works

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2 of 2 papers shown

1.

Grigsby, Jake, et al.. (2021). Deep learning analysis of deeply virtual exclusive photoproduction. Physical review. D. 104(1). 11 indexed citations

2.

Hoskins, J. R. & Kevin J. Roberts. (2018). The Dynamics of the Double Pendulum. Murray State's Digital Commons (Murray State University). 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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