Peter Tiňo
Impact in
- Artificial Intelligence top 0.5%
- Neural Networks and Applications
- Neural Networks and Reservoir Computing
- Anomaly Detection Techniques and Applications
- Machine Learning and ELM
- Signal Processing top 2%
- Time Series Analysis and Forecasting
Papers in
-
- Neural Networks and Applications 49
- Neural Networks and Reservoir Computing 20
Peter Tiňo
189 papers receiving 4.0k citations
Hit Papers
Peers
Comparison fields: 5 of 181
- Artificial Intelligence 2.3k
- Signal Processing 352
- Cognitive Neuroscience 522
- Computer Vision and Pattern Recognition 542
- Management Science and Operations Research 264
Countries citing papers authored by Peter Tiňo
This map shows the geographic impact of Peter Tiňo'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 Peter Tiňo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Tiňo more than expected).
Fields of papers citing papers by Peter Tiňo
This network shows the impact of papers produced by Peter Tiňo. 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 Peter Tiňo. The network helps show where Peter Tiňo may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Peter Tiňo, 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 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 1 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 1 | |
| 7 | 2023 | 4 | |
| 8 | 2023 | 2 | |
| 9 | 2023 | 9 | |
| 10 | 2023 | 3 | |
| 11 | 2023 | 10 | |
| 12 | 2022 | 4 | |
| 13 | 2022 | 20 | |
| 14 | 2022 | 11 | |
| 15 | 2020 | 27 | |
| 16 | 2019 | 14 | |
| 17 | 2018 | 6 | |
| 18 | 2018 | 8 | |
| 19 | 2004 | 19 | |
| 20 | Graded Grammaticality in Prediction Fractal Machines | 1999 | 1 |
About Peter Tiňo
Peter Tiňo is a scholar working on Artificial Intelligence, Computational Mathematics, Cognitive Neuroscience, Computer Vision and Pattern Recognition and Signal Processing, having authored 201 papers that have together received 4.2k indexed citations. Recurring topics across this work include Neural Networks and Applications (49 papers), Neural Networks and Reservoir Computing (20 papers), Neural dynamics and brain function (19 papers), Face and Expression Recognition (18 papers), Advanced Memory and Neural Computing (11 papers), Data Visualization and Analytics (10 papers), Complex Systems and Time Series Analysis (10 papers) and Service-Oriented Architecture and Web Services (9 papers). The work is most often cited by research in Artificial Intelligence (2.3k citations), Signal Processing (352 citations), Cognitive Neuroscience (522 citations), Computer Vision and Pattern Recognition (542 citations) and Management Science and Operations Research (264 citations). Peter Tiňo has collaborated with scholars based in United Kingdom, China and United States. Frequent co-authors include Ali Rodan, Xin Yao, C. Lee Giles, Tsung-Nan Lin, B.G. Horne, Huanhuan Chen, Gavin Brown, Jeremy Wyatt, Georg Dorffner and Zoe Kourtzi. Their work appears in journals such as Neural Computation, Neurocomputing, IEEE Transactions on Neural Networks and Learning Systems, Nature Communications and IEEE Transactions on Knowledge and Data Engineering.
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.