Robert M. Patton

1.8k total citations · 1 hit paper
67 papers, 865 citations indexed

About

Robert M. Patton is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Robert M. Patton has authored 67 papers receiving a total of 865 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Artificial Intelligence, 16 papers in Computer Vision and Pattern Recognition and 16 papers in Electrical and Electronic Engineering. Recurrent topics in Robert M. Patton's work include Advanced Memory and Neural Computing (16 papers), Ferroelectric and Negative Capacitance Devices (13 papers) and Neural Networks and Reservoir Computing (10 papers). Robert M. Patton is often cited by papers focused on Advanced Memory and Neural Computing (16 papers), Ferroelectric and Negative Capacitance Devices (13 papers) and Neural Networks and Reservoir Computing (10 papers). Robert M. Patton collaborates with scholars based in United States, United Kingdom and Chile. Robert M. Patton's co-authors include Steven R. Young, Thomas E. Potok, Derek Rose, Thomas P. Karnowski, Seung–Hwan Lim, Catherine D. Schuman, J. Parker Mitchell, Prasanna Date, James S. Plank and Maryam Parsa and has published in prestigious journals such as Scientific American, Frontiers in Neuroscience and Scientometrics.

In The Last Decade

Robert M. Patton

61 papers receiving 827 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Optimizing deep learning hyper-parameters through an evolutionary algorithm

2015 283 citations Steven R. Young, Derek Rose et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Robert M. Patton United States	15	447	248	153	78	72	67	865
Steven R. Young United States	13	400 0.9×	204 0.8×	161 1.1×	31 0.4×	45 0.6×	36	741
Sergio Gómez Colmenarejo United States	5	494 1.1×	158 0.6×	189 1.2×	87 1.1×	55 0.8×	8	764
Yori Zwólš United States	9	446 1.0×	206 0.8×	160 1.0×	85 1.1×	85 1.2×	15	816
Tim Harley United Kingdom	6	510 1.1×	159 0.6×	190 1.2×	92 1.2×	61 0.8×	7	797
Malcolm Reynolds United Kingdom	5	491 1.1×	155 0.6×	223 1.5×	87 1.1×	54 0.8×	5	809
Phil Blunsom United Kingdom	14	966 2.2×	345 1.4×	235 1.5×	89 1.1×	57 0.8×	29	1.4k
Na Zou United States	17	376 0.8×	103 0.4×	149 1.0×	69 0.9×	62 0.9×	70	1.0k
Evgeny Osipov Sweden	18	394 0.9×	498 2.0×	76 0.5×	41 0.5×	41 0.6×	80	927
Isabelle Guyon France	10	661 1.5×	100 0.4×	177 1.2×	139 1.8×	63 0.9×	15	1.1k
Michael Hüsken Germany	7	423 0.9×	128 0.5×	226 1.5×	57 0.7×	49 0.7×	9	918

Countries citing papers authored by Robert M. Patton

Since Specialization

Citations

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

Fields of papers citing papers by Robert M. Patton

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Robert M. Patton

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

All Works

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20 of 20 papers shown

Young, Steven R., et al.. (2024). Efficacy of using a dynamic length representation vs. a fixed-length for neuroarchitecture search. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 1888–1894.

Date, Prasanna, et al.. (2023). SuperNeuro: A Fast and Scalable Simulator for Neuromorphic Computing. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–4. 4 indexed citations

Patton, Robert M., Catherine D. Schuman, Shruti Kulkarni, et al.. (2021). Neuromorphic Computing for Autonomous Racing. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–5. 19 indexed citations

Date, Prasanna, Bill Kay, Catherine D. Schuman, Robert M. Patton, & Thomas E. Potok. (2021). Computational Complexity of Neuromorphic Algorithms. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–7. 9 indexed citations

Lim, Seung–Hwan, Junghoon Chae, Guojing Cong, et al.. (2021). Visual Understanding of COVID-19 Knowledge Graph for Predictive Analysis. 2021 IEEE International Conference on Big Data (Big Data). 4381–4386. 2 indexed citations

Gao, Shang, et al.. (2021). Diagnosing autonomous vehicle driving criteria with an adversarial evolutionary algorithm. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 301–302. 3 indexed citations

Guan, Hui, et al.. (2020). FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks.. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 2. 247–261. 2 indexed citations

Date, Prasanna, Robert M. Patton, Catherine D. Schuman, & Thomas E. Potok. (2019). Efficiently embedding QUBO problems on adiabatic quantum computers. Quantum Information Processing. 18(4). 46 indexed citations

Chae, Junghoon, Catherine D. Schuman, Steven R. Young, et al.. (2019). Visualization System for Evolutionary Neural Networks for Deep Learning. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 4498–4502. 1 indexed citations

10.

Schuman, Catherine D., James S. Plank, Robert M. Patton, & Thomas E. Potok. (2019). Island model for parallel evolutionary optimization of spiking neuromorphic computing. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 306–307. 3 indexed citations

11.

Parsa, Maryam, J. Parker Mitchell, Catherine D. Schuman, et al.. (2019). Bayesian-based Hyperparameter Optimization for Spiking Neuromorphic Systems. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 4472–4478. 14 indexed citations

12.

Perdue, Gabriel, et al.. (2017). Vertex reconstruction of neutrino interactions using deep learning. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 2275–2281. 6 indexed citations

13.

Young, Steven R., Derek Rose, Travis Johnston, et al.. (2017). Evolving Deep Networks Using HPC. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–7. 30 indexed citations

14.

Potok, Thomas E., Catherine D. Schuman, Steven R. Young, et al.. (2016). A study of complex deep learning networks on high performance, neuromorphic, and quantum computers. IEEE International Conference on High Performance Computing, Data, and Analytics. 47–55. 7 indexed citations

15.

Sukumar, Sreenivas R., et al.. (2015). Using clinical data, hypothesis generation tools and PubMed trends to discover the association between diabetic retinopathy and antihypertensive drugs. 18. 2578–2582. 1 indexed citations

16.

Patton, Robert M., et al.. (2009). Learning cue phrase patterns from radiology reports using a genetic algorithm. 7. 1–4. 1 indexed citations

17.

Patton, Robert M., et al.. (2008). Discovery, analysis, and characteristics of event impacts. International Conference on Information Fusion. 1–8. 1 indexed citations

18.

Patton, Robert M. & Thomas E. Potok. (2006). Characterizing large text corpora using a maximum variation sampling genetic algorithm. 1877–1878. 2 indexed citations

19.

Potok, Thomas E., et al.. (2005). Agent Based Approach for Searching, Mining and Managing Enormous Amounts of Spatial Image Data. The Florida AI Research Society. 351–357. 1 indexed citations

20.

Lilienfeld, Abraham M., et al.. (1951). Accuracy of Supplemental Medical Information on Birth Certificates. Public Health Reports (1896-1970). 66(7). 191–191. 35 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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