Shin‐ichi Maeda

73 papers receiving 3.3k citations

Hit Papers

Virtual Adversarial Training: A Regularization Method for...2018202620202023201850010001.5k

Peers

Shin‐ichi Maeda
Comparison fields: 5 of 159
  • Artificial Intelligence 1.3k
  • Molecular Biology 1.1k
  • Computer Vision and Pattern Recognition 964
  • Renewable Energy, Sustainability and the Environment 635
  • Ecology 390
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Countries citing papers authored by Shin‐ichi Maeda

Since Specialization
Citations

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

Fields of papers citing papers by Shin‐ichi Maeda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shin‐ichi Maeda

This figure shows the co-authorship network connecting the top 25 collaborators of Shin‐ichi Maeda. A scholar is included among the top collaborators of Shin‐ichi Maeda 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‐ichi Maeda. Shin‐ichi Maeda 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
#WorkIndexed citations
1
Robustness to Adversarial Perturbations in Learning from Incomplete Data
5
2
Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks.
8
3
Clipped Action Policy Gradient
1
4
Synthetic Gradient Methods with Virtual Forward-Backward Networks
0
5 2
6
Distributional Smoothing with Virtual Adversarial Training
97
7
Distributional Smoothing by Virtual Adversarial Examples
2
8
Layered Monte-Carlo planning method for incomplete information games and its application to Puyo-Puyo
1
9
Fusion of Multiple Cues from Color and Depth Domains using Occlusion Aware Bayesian Tracker
5
10 1
11 54
12
Control of the falling cat motion by using path-integral reinforcement learning
1
13
Generalized TD Learning
16
14
Generalization of TD-learning from a Semiparametric Statistical Viewpoint
1
15 7
16
1
17 14
18 16
19 26
20
Molecular analysis of the structure-function relationship of the nitrate/nitrite transporter in Synechococcus sp. PCC 7942
2

About Shin‐ichi Maeda

Shin‐ichi Maeda is a scholar working on Structural Biology, Biochemistry and Computer Vision and Pattern Recognition, having authored 80 papers that have together received 3.4k indexed citations. Recurring topics across this work include Photosynthetic Processes and Mechanisms (22 papers), Plant nutrient uptake and metabolism (12 papers) and Algal biology and biofuel production (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (964 citations), Artificial Intelligence (1.3k citations) and Renewable Energy, Sustainability and the Environment (635 citations). Shin‐ichi Maeda has collaborated with scholars based in Japan, United States and Australia. Frequent co-authors include Shin Ishii, Masanori Koyama, Takeru Miyato, Tatsuo Omata, G. Dean Price, Murray R. Badger, Nobuyuki Takatani, Yukari Takahashi, Kan Tanaka and Ryohei Tanigawa. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

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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