Masashi Shimbo
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Statistical and Nonlinear Physics top 5%
- Information Systems top 5%
- Molecular Biology
- Co-authors
- Yūji MatsumotoMarco SaerensHidekazu OiwaToru IshidaAmin MantrachKatsuhiko HayashiFrançois FoussTaku Kudo
- Topics
- Topic Modeling (24 papers)Advanced Graph Neural Networks (16 papers)Natural Language Processing Techniques (15 papers)
- Cited by
- Artificial IntelligenceStatistical and Nonlinear PhysicsComputer Vision and Pattern Recognition
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE AccessPattern Recognition
- Partner nations
- JapanBelgiumUnited States
In The Last Decade
Masashi Shimbo
49 papers receiving 854 citations
Peers
Comparison fields: 5 of 93
- Artificial Intelligence 620
- Computer Vision and Pattern Recognition 203
- Statistical and Nonlinear Physics 200
- Information Systems 119
- Molecular Biology 92
Countries citing papers authored by Masashi Shimbo
This map shows the geographic impact of Masashi Shimbo'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 Masashi Shimbo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Masashi Shimbo more than expected).
Fields of papers citing papers by Masashi Shimbo
This network shows the impact of papers produced by Masashi Shimbo. 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 Masashi Shimbo. The network helps show where Masashi Shimbo may publish in the future.
Co-authorship network of co-authors of Masashi Shimbo
This figure shows the co-authorship network connecting the top 25 collaborators of Masashi Shimbo. A scholar is included among the top collaborators of Masashi Shimbo 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 Masashi Shimbo. Masashi Shimbo 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 | 1 | |
| 3 | 7 | |
| 4 | 0 | |
| 5 | 10 | |
| 6 | 12 | |
| 7 | Using the Mutual k-Nearest Neighbor Graphs for Semi-supervised Classification on Natural Language Data | 40 |
| 8 | HITS-based Seed Selection and Stop List Construction for Bootstrapping | 5 |
| 9 | 2 | |
| 10 | 35 | |
| 11 | Generic Text Summarization Using Probabilistic Latent Semantic Indexing | 16 |
| 12 | A Discriminative Learning Model for Coordinate Conjunctions | 21 |
| 13 | 7 | |
| 14 | Semi - supervised sentence classification for MEDLINE documents | 7 |
| 15 | 1 | |
| 16 | 54 | |
| 17 | 1 | |
| 18 | 1 | |
| 19 | Towards real-time search with inadmissible heuristics | 6 |
| 20 | Improving the learning efficiencies of realtime search | 20 |
About Masashi Shimbo
Masashi Shimbo is a scholar working on Computational Mathematics, Artificial Intelligence and Statistical and Nonlinear Physics, having authored 52 papers that have together received 896 indexed citations. Recurring topics across this work include Topic Modeling (24 papers), Advanced Graph Neural Networks (16 papers) and Natural Language Processing Techniques (15 papers). The work is most often cited by research in Artificial Intelligence (620 citations), Statistical and Nonlinear Physics (200 citations) and Computer Vision and Pattern Recognition (203 citations). Masashi Shimbo has collaborated with scholars based in Japan, Belgium and United States. Frequent co-authors include Yūji Matsumoto, Marco Saerens, Hidekazu Oiwa, Toru Ishida, Amin Mantrach, Katsuhiko Hayashi, François Fouss, Taku Kudo, Kazuo Hara and Mamoru Komachi. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Access and Pattern Recognition.
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.