Tom Maxwell
Impact in
- Artificial Intelligence top 2%
- Neural Networks and Applications
- Fuzzy Logic and Control Systems
- Neural Networks and Reservoir Computing
- Machine Learning and ELM
- Signal Processing top 10%
- Blind Source Separation Techniques
Papers in
-
- Neural Networks and Applications 7
- Fuzzy Logic and Control Systems 1
- Machine Learning and Algorithms 1
-
- Neural Networks Stability and Synchronization 2
- Co-authors
- Clyde Lee Giles (6 shared papers)Guo-Zheng Sun (1 shared paper)Gary D. Doolen (1 shared paper)Daniel Griffin (1 shared paper)John F. Helliwell (1 shared paper)
- Journals
- Canadian Journal of Economics/Revue canadienne d économique (2 papers)Physica D Nonlinear Phenomena (1 paper)Applied Optics (1 paper)AIP conference proceedings (2 papers)WORLD SCIENTIFIC eBooks (1 paper)
- Partner nations
- United StatesFrance
In The Last Decade
Tom Maxwell
9 papers receiving 797 citations
Peers
Comparison fields: 5 of 66
- Artificial Intelligence 694
- Signal Processing 107
- Computer Vision and Pattern Recognition 198
- Statistical and Nonlinear Physics 66
- Management Science and Operations Research 63
Countries citing papers authored by Tom Maxwell
This map shows the geographic impact of Tom Maxwell'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 Tom Maxwell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Maxwell more than expected).
Fields of papers citing papers by Tom Maxwell
This network shows the impact of papers produced by Tom Maxwell. 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 Tom Maxwell. The network helps show where Tom Maxwell may publish in the future.
Co-authors
The 5 scholars most cited alongside Tom Maxwell, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 1987 | 550 | |
| 2 | 1986 | 131 | |
| 3 | Encoding Geometric Invariances in Higher-Order Neural Networks | 1987 | 58 |
| 4 | 1986 | 45 | |
| 5 | 1986 | 30 | |
| 6 | GENERALIZATION IN NEURAL NETWORKS: THE CONTIGUITY PROBLEM. | 1987 | 21 |
| 7 | Nonlinear dynamics of artificial neural systems | 1987 | 14 |
| 8 | 1979 | 2 | |
| 9 | 1979 | 2 | |
| 10 | 1989 | 2 |
About Tom Maxwell
Tom Maxwell is a scholar working on Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture, Cognitive Neuroscience and Computer Vision and Pattern Recognition, having authored 10 papers that have together received 855 indexed citations. Recurring topics across this work include Neural Networks and Applications (7 papers), Neural Networks Stability and Synchronization (2 papers), Face and Expression Recognition (1 paper), Fuzzy Logic and Control Systems (1 paper), Embedded Systems Design Techniques (1 paper), Machine Learning and Algorithms (1 paper), Neural dynamics and brain function (1 paper) and Control and Stability of Dynamical Systems (1 paper). The work is most often cited by research in Artificial Intelligence (694 citations), Signal Processing (107 citations), Computer Vision and Pattern Recognition (198 citations), Statistical and Nonlinear Physics (66 citations) and Management Science and Operations Research (63 citations). Tom Maxwell has collaborated with scholars based in United States and France. Frequent co-authors include Clyde Lee Giles, Guo-Zheng Sun, Gary D. Doolen, Daniel Griffin and John F. Helliwell. Their work appears in journals such as Canadian Journal of Economics/Revue canadienne d économique, Physica D Nonlinear Phenomena, Applied Optics, AIP conference proceedings and WORLD SCIENTIFIC eBooks.
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.