Shin Ishii

10.5k total citations · 2 hit papers
234 papers, 6.3k citations indexed

About

Shin Ishii is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Computer Vision and Pattern Recognition. According to data from OpenAlex, Shin Ishii has authored 234 papers receiving a total of 6.3k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Artificial Intelligence, 65 papers in Cognitive Neuroscience and 43 papers in Computer Vision and Pattern Recognition. Recurrent topics in Shin Ishii's work include Neural dynamics and brain function (41 papers), Reinforcement Learning in Robotics (24 papers) and Neural Networks and Applications (24 papers). Shin Ishii is often cited by papers focused on Neural dynamics and brain function (41 papers), Reinforcement Learning in Robotics (24 papers) and Neural Networks and Applications (24 papers). Shin Ishii collaborates with scholars based in Japan, United States and Germany. Shin Ishii's co-authors include Shin‐ichi Maeda, Masanori Koyama, Takeru Miyato, Masa-aki Sato, Shigeyuki Oba, Wako Yoshida, Ichiro Takemasa, Haruo Kasai, Morito Monden and Hidetoshi Urakubo and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Shin Ishii

220 papers receiving 6.0k citations

Hit Papers

Virtual Adversarial Training:... 2003 2026 2010 2018 2018 2003 500 1000 1.5k

Peers

Shin Ishii
Comparison fields: 5 of 193
  • Artificial Intelligence 2.1k
  • Computer Vision and Pattern Recognition 1.3k
  • Cognitive Neuroscience 1.3k
  • Molecular Biology 1.2k
  • Cellular and Molecular Neuroscience 788
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Fuhui Long United States View profile →
Citations per field, relative to Shin Ishii
Shin Ishii · 1×
Citations per year, relative to Shin Ishii
Shin Ishii · 1×

Countries citing papers authored by Shin Ishii

Since Specialization
Citations

This map shows the geographic impact of Shin Ishii'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 Shin Ishii with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shin Ishii more than expected).

Fields of papers citing papers by Shin Ishii

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shin Ishii. 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 Shin Ishii. The network helps show where Shin Ishii may publish in the future.

Co-authorship network of co-authors of Shin Ishii

This figure shows the co-authorship network connecting the top 25 collaborators of Shin Ishii. A scholar is included among the top collaborators of Shin Ishii 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 Shin Ishii. Shin Ishii is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 1
2 21
3 2
4 0
5 1
6 5
7 1
8 15
9 4
10 401
11
Layered Monte-Carlo planning method for incomplete information games and its application to Puyo-Puyo
1
12
Enhancing Probabilistic Appearance-Based Object Tracking with Depth Information: Object Tracking under Occlusion
1
13
Control of the falling cat motion by using path-integral reinforcement learning
1
14
A unified framework of binary classifiers ensemble for multi-class classification
2
15
Generalized TD Learning
16
16
Reinforcement learning for a CPG-driven biped robot
47
17 3
18
Learning of minimax strategy by a support vector machine
1
19
Strategy Acquisition for the Game "Othello" Based on Reinforcement Learning
14
20
On-Line EM Algorithm for Mixture of Local Experts
4

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.

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