Ken Nakae
Impact in
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- Brain Tumor Detection and Classification
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- Advanced Neural Network Applications
Papers in
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- Neural dynamics and brain function 9
- Functional Brain Connectivity Studies 9
- Memory and Neural Mechanisms 3
- Co-authors
- Shin Ishii (15 shared papers)Masanori Koyama (3 shared papers)Takeru Miyato (2 shared papers)Shin‐ichi Maeda (2 shared papers)Junichi Hata (8 shared papers)Hideyuki Okano (8 shared papers)Ryo Ito (1 shared paper)Honda Naoki (3 shared papers)
In The Last Decade
Ken Nakae
20 papers receiving 263 citations
Peers
Comparison fields: 5 of 75
- Neurology 34
- Computer Vision and Pattern Recognition 76
- Artificial Intelligence 111
- Cognitive Neuroscience 62
- Biophysics 18
Countries citing papers authored by Ken Nakae
This map shows the geographic impact of Ken Nakae'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 Ken Nakae with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ken Nakae more than expected).
Fields of papers citing papers by Ken Nakae
This network shows the impact of papers produced by Ken Nakae. 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 Ken Nakae. The network helps show where Ken Nakae may publish in the future.
Co-authors
The 25 scholars most cited alongside Ken Nakae, 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 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Distributional Smoothing with Virtual Adversarial Training | 2016 | 97 |
| 2 | 2019 | 59 | |
| 3 | 2023 | 22 | |
| 4 | 2020 | 15 | |
| 5 | 2016 | 14 | |
| 6 | 2010 | 11 | |
| 7 | 2018 | 11 | |
| 8 | 2023 | 10 | |
| 9 | 2021 | 9 | |
| 10 | 2020 | 6 | |
| 11 | 2014 | 6 | |
| 12 | 2019 | 4 | |
| 13 | 2024 | 3 | |
| 14 | 2025 | 2 | |
| 15 | 2018 | 2 | |
| 16 | 2023 | 2 | |
| 17 | Distributional Smoothing by Virtual Adversarial Examples | 2015 | 2 |
| 18 | 2025 | 1 | |
| 19 | 2016 | 1 | |
| 20 | 2021 | 1 |
About Ken Nakae
Ken Nakae is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Molecular Biology, having authored 22 papers that have together received 278 indexed citations. Recurring topics across this work include Neural dynamics and brain function (9 papers), Functional Brain Connectivity Studies (9 papers), Advanced Neuroimaging Techniques and Applications (4 papers), Advanced MRI Techniques and Applications (3 papers), Single-cell and spatial transcriptomics (3 papers), Memory and Neural Mechanisms (3 papers), Nonlinear Dynamics and Pattern Formation (2 papers) and Medical Image Segmentation Techniques (2 papers). The work is most often cited by research in Neurology (34 citations), Computer Vision and Pattern Recognition (76 citations), Artificial Intelligence (111 citations), Cognitive Neuroscience (62 citations) and Biophysics (18 citations). Ken Nakae has collaborated with scholars based in Japan, Germany and Indonesia. Frequent co-authors include Shin Ishii, Masanori Koyama, Takeru Miyato, Shin‐ichi Maeda, Junichi Hata, Hideyuki Okano, Ryo Ito, Honda Naoki, Henrik Skibbe and Tetsuo Yamamori. Their work appears in journals such as Scientific Reports, Nature Communications, PLoS Computational Biology, Neural Networks and Journal of the Physical Society of Japan.
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.