Hideitsu Hino
Impact in
- Structural Biology top 10%
- Statistics and Probability top 5%
Papers in
-
- Neural Networks and Applications 9
- Domain Adaptation and Few-Shot Learning 6
- Gaussian Processes and Bayesian Inference 6
-
- Face and Expression Recognition 11
- Co-authors
- Noboru Murata (37 shared papers)Kanta Ono (10 shared papers)Masato Kotsugi (4 shared papers)Yuta Suzuki (3 shared papers)Kotaro Saito (2 shared papers)Toshiyuki Kato (3 shared papers)Tetsuro Ueno (5 shared papers)Takao Murakami (3 shared papers)
- Journals
- Neural Computation (12 papers)Neural Networks (7 papers)Scientific Reports (4 papers)npj Computational Materials (3 papers)Neurocomputing (2 papers)
- Partner nations
- JapanUnited StatesFinland
In The Last Decade
Hideitsu Hino
84 papers receiving 666 citations
Peers
Comparison fields: 5 of 115
- Structural Biology 16
- Statistics and Probability 49
- Artificial Intelligence 178
- Computer Vision and Pattern Recognition 99
- Surfaces, Coatings and Films 29
Countries citing papers authored by Hideitsu Hino
This map shows the geographic impact of Hideitsu Hino'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 Hideitsu Hino with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hideitsu Hino more than expected).
Fields of papers citing papers by Hideitsu Hino
This network shows the impact of papers produced by Hideitsu Hino. 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 Hideitsu Hino. The network helps show where Hideitsu Hino may publish in the future.
Co-authors
The 25 scholars most cited alongside Hideitsu Hino, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 93 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 93 | |
| 2 | 2013 | 56 | |
| 3 | 2014 | 50 | |
| 4 | 2018 | 40 | |
| 5 | 2019 | 35 | |
| 6 | 2015 | 27 | |
| 7 | 2021 | 21 | |
| 8 | 2012 | 19 | |
| 9 | 2018 | 17 | |
| 10 | 2017 | 16 | |
| 11 | 2022 | 16 | |
| 12 | 2010 | 16 | |
| 13 | 2020 | 15 | |
| 14 | 2019 | 15 | |
| 15 | 2017 | 12 | |
| 16 | 2016 | 12 | |
| 17 | 2013 | 11 | |
| 18 | 2020 | 11 | |
| 19 | 2020 | 9 | |
| 20 | 2022 | 9 |
About Hideitsu Hino
Hideitsu Hino is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability, Signal Processing and Materials Chemistry, having authored 93 papers that have together received 684 indexed citations. Recurring topics across this work include Face and Expression Recognition (11 papers), Neural Networks and Applications (9 papers), Image Processing Techniques and Applications (7 papers), Machine Learning in Materials Science (7 papers), Advanced Statistical Methods and Models (7 papers), Domain Adaptation and Few-Shot Learning (6 papers), Gaussian Processes and Bayesian Inference (6 papers) and earthquake and tectonic studies (5 papers). The work is most often cited by research in Structural Biology (16 citations), Statistics and Probability (49 citations), Artificial Intelligence (178 citations), Computer Vision and Pattern Recognition (99 citations) and Surfaces, Coatings and Films (29 citations). Hideitsu Hino has collaborated with scholars based in Japan, United States and Finland. Frequent co-authors include Noboru Murata, Kanta Ono, Masato Kotsugi, Yuta Suzuki, Kotaro Saito, Toshiyuki Kato, Tetsuro Ueno, Takao Murakami, Yasuhiro Hayashi and Shinji Wakao. Their work appears in journals such as Neural Computation, Neural Networks, Scientific Reports, npj Computational Materials and Neurocomputing.
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.