Yudai Suzuki
- Artificial Intelligence top 10%
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition
- Atomic and Molecular Physics, and Optics
- Cognitive Neuroscience
- Co-authors
- Naoki YamamotoJunji MaedaLes AtlasKenji YasuokaKen C. PradelKohei NakajimaVo AnhA. Marçal
- Topics
- Neural Networks and Applications (9 papers)Quantum Computing Algorithms and Architecture (7 papers)Image Retrieval and Classification Techniques (6 papers)
- Journals
- SHILAP Revista de lepidopterologíaProceedings of the IEEEScientific Reports
- Partner nations
- JapanAustraliaUnited States
In The Last Decade
Yudai Suzuki
35 papers receiving 235 citations
Peers
Comparison fields: 5 of 59
- Artificial Intelligence 137
- Electrical and Electronic Engineering 81
- Computer Vision and Pattern Recognition 56
- Atomic and Molecular Physics, and Optics 20
- Cognitive Neuroscience 15
Countries citing papers authored by Yudai Suzuki
This map shows the geographic impact of Yudai Suzuki'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 Yudai Suzuki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yudai Suzuki more than expected).
Fields of papers citing papers by Yudai Suzuki
This network shows the impact of papers produced by Yudai Suzuki. 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 Yudai Suzuki. The network helps show where Yudai Suzuki may publish in the future.
Co-authorship network of co-authors of Yudai Suzuki
This figure shows the co-authorship network connecting the top 25 collaborators of Yudai Suzuki. A scholar is included among the top collaborators of Yudai Suzuki 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 Yudai Suzuki. Yudai Suzuki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 5 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 8 | |
| 8 | 0 | |
| 9 | 2 | |
| 10 | 9 | |
| 11 | 2 | |
| 12 | 1 | |
| 13 | 6 | |
| 14 | 0 | |
| 15 | 48 | |
| 16 | 0 | |
| 17 | Analyzing feature space via pauli decomposition for quantum classifier | 1 |
| 18 | 2 | |
| 19 | 3 | |
| 20 | 21 |
About Yudai Suzuki
Yudai Suzuki is a scholar working on Industrial and Manufacturing Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 44 papers that have together received 247 indexed citations. Recurring topics across this work include Neural Networks and Applications (9 papers), Quantum Computing Algorithms and Architecture (7 papers) and Image Retrieval and Classification Techniques (6 papers). The work is most often cited by research in Artificial Intelligence (137 citations), Computer Vision and Pattern Recognition (56 citations) and Media Technology (13 citations). Yudai Suzuki has collaborated with scholars based in Japan, Australia and United States. Frequent co-authors include Naoki Yamamoto, Junji Maeda, Les Atlas, Kenji Yasuoka, Ken C. Pradel, Kohei Nakajima, Vo Anh, A. Marçal, Ikko Hamamura and Teresa Mendonça. Their work appears in journals such as SHILAP Revista de lepidopterología, Proceedings of the IEEE and Scientific Reports.
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.