Tohru Nitta
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Computer Networks and Communications top 5%
- Signal Processing top 5%
- Electrical and Electronic Engineering
- Co-authors
- Yasuaki KuroeEckhard HitzerDanilo P. MandicSven BuchholzYili XiaCyrus JahanchahiMasaki KobayashiTakayuki Hoshino
- Topics
- Neural Networks and Applications (33 papers)Face and Expression Recognition (9 papers)Blind Source Separation Techniques (8 papers)
- Journals
- IEEE Transactions on Signal ProcessingNeural ComputationIEEE Transactions on Neural Networks and Learning Systems
- Partner nations
- JapanUnited KingdomGermany
In The Last Decade
Tohru Nitta
41 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 80
- Artificial Intelligence 758
- Computer Vision and Pattern Recognition 344
- Computer Networks and Communications 288
- Signal Processing 223
- Electrical and Electronic Engineering 167
Countries citing papers authored by Tohru Nitta
This map shows the geographic impact of Tohru Nitta'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 Tohru Nitta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tohru Nitta more than expected).
Fields of papers citing papers by Tohru Nitta
This network shows the impact of papers produced by Tohru Nitta. 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 Tohru Nitta. The network helps show where Tohru Nitta may publish in the future.
Co-authorship network of co-authors of Tohru Nitta
This figure shows the co-authorship network connecting the top 25 collaborators of Tohru Nitta. A scholar is included among the top collaborators of Tohru Nitta 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 Tohru Nitta. Tohru Nitta 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 | 0 | |
| 3 | 23 | |
| 4 | 7 | |
| 5 | 2 | |
| 6 | 7 | |
| 7 | 2 | |
| 8 | 6 | |
| 9 | 32 | |
| 10 | 2 | |
| 11 | 127 | |
| 12 | 1 | |
| 13 | 7 | |
| 14 | 178 | |
| 15 | 8 | |
| 16 | 12 | |
| 17 | An Extension of the Back-propagation Algorithm to Quaternions | 26 |
| 18 | Quaternary version of the back-propagation algorithm | 4 |
| 19 | Behaviour of the complex numbered backpropagation network which has learned similar transformation | 1 |
| 20 | Solvability and state equations of RCG networks | 1 |
About Tohru Nitta
Tohru Nitta is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition, having authored 44 papers that have together received 1.2k indexed citations. Recurring topics across this work include Neural Networks and Applications (33 papers), Face and Expression Recognition (9 papers) and Blind Source Separation Techniques (8 papers). The work is most often cited by research in Artificial Intelligence (758 citations), Signal Processing (223 citations) and Computer Vision and Pattern Recognition (344 citations). Tohru Nitta has collaborated with scholars based in Japan, United Kingdom and Germany. Frequent co-authors include Yasuaki Kuroe, Eckhard Hitzer, Danilo P. Mandic, Sven Buchholz, Yili Xia, Cyrus Jahanchahi, Masaki Kobayashi, Takayuki Hoshino and Suguru Kanoga. Their work appears in journals such as IEEE Transactions on Signal Processing, Neural Computation and IEEE Transactions on Neural Networks and Learning 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.