Tom Maxwell
Impact in
- Artificial Intelligence top 2%
- Neural Networks and Applications
- Neural Networks and Reservoir Computing
- Fuzzy Logic and Control Systems
- Machine Learning and ELM
- Signal Processing top 10%
- Blind Source Separation Techniques
Papers in ⓘ
-
- Neural Networks and Applications 7
- Machine Learning and Algorithms 1
- Fuzzy Logic and Control Systems 1
-
- Neural Networks Stability and Synchronization 2
- Co-authors
- C. Lee Giles (6 shared papers)Daniel Griffin (1 shared paper)Guo-Zheng Sun (1 shared paper)Gary D. Doolen (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)AIP conference proceedings (2 papers)Applied Optics (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesFrance
In The Last Decade
Tom Maxwell
8 papers receiving 649 citations
Peers
Comparison fields: 5 of 63
- Artificial Intelligence 556
- Signal Processing 91
- Computer Vision and Pattern Recognition 157
- Computational Mathematics 3
- Statistical and Nonlinear Physics 54
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 | 437 | |
| 2 | 1986 | 103 | |
| 3 | Encoding Geometric Invariances in Higher-Order Neural Networks | 1987 | 50 |
| 4 | 1986 | 41 | |
| 5 | 1986 | 27 | |
| 6 | GENERALIZATION IN NEURAL NETWORKS: THE CONTIGUITY PROBLEM. | 1987 | 18 |
| 7 | Nonlinear dynamics of artificial neural systems | 1987 | 13 |
| 8 | 1979 | 2 | |
| 9 | 1979 | 2 |
About Tom Maxwell
Tom Maxwell is a scholar working on Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering, Computer Vision and Pattern Recognition and Cognitive Neuroscience, having authored 9 papers that have together received 693 indexed citations. Recurring topics across this work include Neural Networks and Applications (7 papers), Neural Networks Stability and Synchronization (2 papers), Embedded Systems Design Techniques (1 paper), Face and Expression Recognition (1 paper), Machine Learning and Algorithms (1 paper), Control and Stability of Dynamical Systems (1 paper), Fuzzy Logic and Control Systems (1 paper) and Neural dynamics and brain function (1 paper). The work is most often cited by research in Artificial Intelligence (556 citations), Signal Processing (91 citations), Computer Vision and Pattern Recognition (157 citations), Computational Mathematics (3 citations) and Statistical and Nonlinear Physics (54 citations). Tom Maxwell has collaborated with scholars based in United States and France. Frequent co-authors include C. Lee Giles, Daniel Griffin, Guo-Zheng Sun, Gary D. Doolen and John F. Helliwell. Their work appears in journals such as Canadian Journal of Economics/Revue canadienne d économique, Physica D Nonlinear Phenomena, AIP conference proceedings, Applied Optics and Neural Information Processing Systems.
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.