Gary M. Kuhn
- Artificial Intelligence top 5%
- Cognitive Neuroscience top 10%
- Signal Processing top 10%
- Electrical and Electronic Engineering
- Experimental and Cognitive Psychology
- Co-authors
- Raymond L. WatrousRonald J. WilliamsC. Lee GilesBruce LadendorfPaolo IenneStephen José HansonN.I. SantosoThomas Petsche
- Topics
- Neural Networks and Applications (10 papers)Speech and Audio Processing (7 papers)Speech Recognition and Synthesis (5 papers)
- Partner nations
- United StatesSwitzerlandGermany
In The Last Decade
Gary M. Kuhn
20 papers receiving 537 citations
Peers
Comparison fields: 5 of 95
- Artificial Intelligence 352
- Cognitive Neuroscience 136
- Signal Processing 78
- Electrical and Electronic Engineering 72
- Experimental and Cognitive Psychology 65
Countries citing papers authored by Gary M. Kuhn
This map shows the geographic impact of Gary M. Kuhn'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 Gary M. Kuhn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gary M. Kuhn more than expected).
Fields of papers citing papers by Gary M. Kuhn
This network shows the impact of papers produced by Gary M. Kuhn. 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 Gary M. Kuhn. The network helps show where Gary M. Kuhn may publish in the future.
Co-authorship network of co-authors of Gary M. Kuhn
This figure shows the co-authorship network connecting the top 25 collaborators of Gary M. Kuhn. A scholar is included among the top collaborators of Gary M. Kuhn 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 Gary M. Kuhn. Gary M. Kuhn is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | TDT-2004: ADAPTIVE TOPIC TRACKING AT MARYLAND | 1 |
| 3 | 0 | |
| 4 | 10 | |
| 5 | 2 | |
| 6 | 26 | |
| 7 | A Neural Network Autoassociator for Induction Motor Failure Prediction | 32 |
| 8 | 18 | |
| 9 | 151 | |
| 10 | Some variations on training of recurrent networks | 9 |
| 11 | 98 | |
| 12 | Induction of Finite-State Automata Using Second-Order Recurrent Networks | 26 |
| 13 | 18 | |
| 14 | 21 | |
| 15 | 1 | |
| 16 | 2 | |
| 17 | 11 | |
| 18 | 4 | |
| 19 | 104 | |
| 20 | 1 |
About Gary M. Kuhn
Gary M. Kuhn is a scholar working on General Engineering, Signal Processing and Artificial Intelligence, having authored 22 papers that have together received 594 indexed citations. Recurring topics across this work include Neural Networks and Applications (10 papers), Speech and Audio Processing (7 papers) and Speech Recognition and Synthesis (5 papers). The work is most often cited by research in Artificial Intelligence (352 citations), Signal Processing (78 citations) and Cognitive Neuroscience (136 citations). Gary M. Kuhn has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Raymond L. Watrous, Ronald J. Williams, C. Lee Giles, Bruce Ladendorf, Paolo Ienne, Stephen José Hanson, N.I. Santoso, Thomas Petsche, Christian J. Darken and P. Mermelstein. Their work appears in journals such as The Journal of the Acoustical Society of America, Medical Physics and Neural Computation.
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.