Michael S. Gashler

632 total citations
16 papers, 392 citations indexed

About

Michael S. Gashler is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Michael S. Gashler has authored 16 papers receiving a total of 392 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 5 papers in Signal Processing. Recurrent topics in Michael S. Gashler's work include Neural Networks and Applications (10 papers), Time Series Analysis and Forecasting (4 papers) and Stock Market Forecasting Methods (2 papers). Michael S. Gashler is often cited by papers focused on Neural Networks and Applications (10 papers), Time Series Analysis and Forecasting (4 papers) and Stock Market Forecasting Methods (2 papers). Michael S. Gashler collaborates with scholars based in United States, Germany and Hungary. Michael S. Gashler's co-authors include Harry A. Pierson, Dan Ventura, Tony Martinez, Charles Xie, Zhenghui Sha, Gábor Csiszár, Владик Крейнович and Michael R. Smith and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing and Journal of Machine Learning Research.

In The Last Decade

Michael S. Gashler

16 papers receiving 373 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michael S. Gashler United States	8	161	108	73	72	40	16	392
Ioan-Daniel Borlea Romania	7	208 1.3×	84 0.8×	69 0.9×	129 1.8×	26 0.7×	17	473
Tuomas Haarnoja Finland	7	256 1.6×	80 0.7×	69 0.9×	119 1.7×	35 0.9×	19	424
Er Meng Joo Singapore	10	159 1.0×	57 0.5×	77 1.1×	106 1.5×	19 0.5×	26	385
Alexandra-Bianca Borlea Romania	5	180 1.1×	88 0.8×	29 0.4×	72 1.0×	30 0.8×	11	408
Jonathan Lee Taiwan	13	163 1.0×	83 0.8×	25 0.3×	106 1.5×	26 0.7×	52	410
Xiao-Zhi Gao Finland	13	261 1.6×	54 0.5×	76 1.0×	81 1.1×	16 0.4×	38	482
Masumi Ishikawa Japan	10	284 1.8×	92 0.9×	29 0.4×	60 0.8×	25 0.6×	49	448
Erkan Tanyıldızı Türkiye	9	233 1.4×	125 1.2×	78 1.1×	77 1.1×	14 0.3×	34	536
Enrique García Mexico	11	121 0.8×	78 0.7×	47 0.6×	179 2.5×	16 0.4×	16	411

Countries citing papers authored by Michael S. Gashler

Since Specialization

Citations

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

Fields of papers citing papers by Michael S. Gashler

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael S. Gashler. 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 Michael S. Gashler. The network helps show where Michael S. Gashler may publish in the future.

Co-authorship network of co-authors of Michael S. Gashler

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

All Works

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16 of 16 papers shown

Csiszár, Gábor, et al.. (2022). Parametric activation functions modelling fuzzy connectives for better explainability of neural models. OPUS (Aalen University). 91. 77–82. 3 indexed citations

Gashler, Michael S., et al.. (2022). Uninorm-like parametric activation functions for human-understandable neural models. Knowledge-Based Systems. 260. 110095–110095. 6 indexed citations

Gashler, Michael S., et al.. (2018). Automatic Clustering of Sequential Design Behaviors. 10 indexed citations

Gashler, Michael S., et al.. (2017). An Investigation of How Neural Networks Learn from the Experiences of Peers Through Periodic Weight Averaging. 731–736. 3 indexed citations

Pierson, Harry A. & Michael S. Gashler. (2017). Deep learning in robotics: a review of recent research. Advanced Robotics. 31(16). 821–835. 217 indexed citations

Pierson, Harry A. & Michael S. Gashler. (2017). Deep Learning in Robotics: A Review of Recent Research. arXiv (Cornell University). 2 indexed citations

Gashler, Michael S., et al.. (2017). Neural Decomposition of Time-Series Data for Effective Generalization. IEEE Transactions on Neural Networks and Learning Systems. 29(7). 1–13. 37 indexed citations

Gashler, Michael S., et al.. (2017). Neural decomposition of time-series data. 3. 2796–2801. 4 indexed citations

Gashler, Michael S., et al.. (2016). Practical Techniques for Using Neural Networks to Estimate State from Images. 25. 916–919. 2 indexed citations

10.

Gashler, Michael S., et al.. (2015). A Continuum among Logarithmic, Linear, and Exponential Functions, and Its Potential to Improve Generalization in Neural Networks. 481–486. 16 indexed citations

11.

Gashler, Michael S., et al.. (2015). A method for finding similarity between multi-layer perceptrons by Forward Bipartite Alignment. 1–7. 3 indexed citations

12.

Gashler, Michael S., et al.. (2015). Modeling time series data with deep Fourier neural networks. Neurocomputing. 188. 3–11. 28 indexed citations

13.

Gashler, Michael S., et al.. (2015). A minimal architecture for general cognition. 25. 1–8. 1 indexed citations

14.

Gashler, Michael S.. (2011). Waffles : A Machine Learning Toolkit. Journal of Machine Learning Research. 12(69). 2383–2387. 24 indexed citations

15.

Gashler, Michael S., Dan Ventura, & Tony Martinez. (2011). Manifold Learning by Graduated Optimization. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 41(6). 1458–1470. 12 indexed citations

16.

Gashler, Michael S., Dan Ventura, & Tony Martinez. (2007). Iterative Non-linear Dimensionality Reduction with Manifold Sculpting. Neural Information Processing Systems. 20. 513–520. 24 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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