Tadahiro Taniguchi
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Control and Systems Engineering top 5%
- Cognitive Neuroscience top 10%
- Signal Processing top 5%
- Co-authors
- Takashi BandoShogo NagasakaKazuhito TakenakaAkira TaniguchiYoshinobu HagiwaraTakayuki NagaiHailong LiuNaoto Iwahashi
- Topics
- Robot Manipulation and Learning (26 papers)Multimodal Machine Learning Applications (23 papers)Reinforcement Learning in Robotics (21 papers)
- Partner nations
- JapanUnited StatesUnited Kingdom
In The Last Decade
Tadahiro Taniguchi
157 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 107
- Artificial Intelligence 717
- Computer Vision and Pattern Recognition 388
- Control and Systems Engineering 329
- Cognitive Neuroscience 227
- Signal Processing 220
Countries citing papers authored by Tadahiro Taniguchi
This map shows the geographic impact of Tadahiro Taniguchi'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 Tadahiro Taniguchi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tadahiro Taniguchi more than expected).
Fields of papers citing papers by Tadahiro Taniguchi
This network shows the impact of papers produced by Tadahiro Taniguchi. 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 Tadahiro Taniguchi. The network helps show where Tadahiro Taniguchi may publish in the future.
Co-authorship network of co-authors of Tadahiro Taniguchi
This figure shows the co-authorship network connecting the top 25 collaborators of Tadahiro Taniguchi. A scholar is included among the top collaborators of Tadahiro Taniguchi 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 Tadahiro Taniguchi. Tadahiro Taniguchi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 34 | |
| 7 | 3 | |
| 8 | 1 | |
| 9 | 5 | |
| 10 | 3 | |
| 11 | 10 | |
| 12 | 33 | |
| 13 | Variational Inference MPC for Bayesian Model-based Reinforcement Learning | 4 |
| 14 | 35 | |
| 15 | Automated Linear Function Submission-based Double Auction for Emergent Real-Time Pricing in a Regional Smart Grid. | 2 |
| 16 | 3 | |
| 17 | A study of communication scheme for media biotope | 3 |
| 18 | 1 | |
| 19 | 0 | |
| 20 | 1 |
About Tadahiro Taniguchi
Tadahiro Taniguchi is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 177 papers that have together received 1.6k indexed citations. Recurring topics across this work include Robot Manipulation and Learning (26 papers), Multimodal Machine Learning Applications (23 papers) and Reinforcement Learning in Robotics (21 papers). The work is most often cited by research in Cultural Studies (195 citations), Artificial Intelligence (717 citations) and Signal Processing (220 citations). Tadahiro Taniguchi has collaborated with scholars based in Japan, United States and United Kingdom. Frequent co-authors include Takashi Bando, Shogo Nagasaka, Kazuhito Takenaka, Akira Taniguchi, Yoshinobu Hagiwara, Takayuki Nagai, Hailong Liu, Naoto Iwahashi, Tomoaki Nakamura and Tetsuo Sawaragi. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Sensors.
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.