Anthony Ndirango
Impact in
- Artificial Intelligence top 10%
- Cognitive Computing and Networks
- Cognitive Science and Mapping
- Neural Networks and Reservoir Computing
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- Neural dynamics and brain function
Papers in
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- Neural Networks and Reservoir Computing 2
- Domain Adaptation and Few-Shot Learning 1
- Adversarial Robustness in Machine Learning 1
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- Statistical Mechanics and Entropy 1
- Co-authors
- Dharmendra S. Modha (2 shared papers)Steven K. Esser (1 shared paper)Raghavendra Singh (1 shared paper)R. Ananthanarayanan (1 shared paper)Anthony J. Sherbondy (1 shared paper)Tyler Lee (2 shared papers)Cory Stephenson (1 shared paper)Oğuz H. Elibol (1 shared paper)
- Journals
- Journal of High Energy Physics (1 paper)IEEE Access (1 paper)Communications of the ACM (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesIndia
In The Last Decade
Anthony Ndirango
5 papers receiving 294 citations
Peers
Comparison fields: 5 of 77
- Artificial Intelligence 127
- Cognitive Neuroscience 44
- Computer Vision and Pattern Recognition 39
- Management Information Systems 17
- Computer Science Applications 10
Countries citing papers authored by Anthony Ndirango
This map shows the geographic impact of Anthony Ndirango'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 Anthony Ndirango with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anthony Ndirango more than expected).
Fields of papers citing papers by Anthony Ndirango
This network shows the impact of papers produced by Anthony Ndirango. 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 Anthony Ndirango. The network helps show where Anthony Ndirango may publish in the future.
Co-authors
The 13 scholars most cited alongside Anthony Ndirango, 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 | 2011 | 205 | |
| 2 | 2019 | 81 | |
| 3 | 2007 | 12 | |
| 4 | 2010 | 7 | |
| 5 | Generalization in multitask deep neural classifiers: a statistical physics approach | 2019 | 3 |
About Anthony Ndirango
Anthony Ndirango is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Cognitive Neuroscience, Electrical and Electronic Engineering and Cellular and Molecular Neuroscience, having authored 5 papers that have together received 308 indexed citations. Recurring topics across this work include Neural Networks and Reservoir Computing (2 papers), Neural dynamics and brain function (2 papers), Advanced Memory and Neural Computing (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Black Holes and Theoretical Physics (1 paper), Cosmology and Gravitation Theories (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Statistical Mechanics and Entropy (1 paper). The work is most often cited by research in Artificial Intelligence (127 citations), Cognitive Neuroscience (44 citations), Computer Vision and Pattern Recognition (39 citations), Management Information Systems (17 citations) and Computer Science Applications (10 citations). Anthony Ndirango has collaborated with scholars based in United States and India. Frequent co-authors include Dharmendra S. Modha, Steven K. Esser, Raghavendra Singh, R. Ananthanarayanan, Anthony J. Sherbondy, Tyler Lee, Cory Stephenson, Oğuz H. Elibol, Gokce Keskin and Bom Soo Kim. Their work appears in journals such as Journal of High Energy Physics, IEEE Access, Communications of the ACM 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.