Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
20181.6k citationsTakeru Miyato, Shin‐ichi Maeda et al.IEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
Citations per year, relative to Takeru Miyato Takeru Miyato (= 1×)
peers
Qianru Sun
Countries citing papers authored by Takeru Miyato
Since
Specialization
Citations
This map shows the geographic impact of Takeru Miyato'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 Takeru Miyato with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takeru Miyato more than expected).
This network shows the impact of papers produced by Takeru Miyato. 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 Takeru Miyato. The network helps show where Takeru Miyato may publish in the future.
Co-authorship network of co-authors of Takeru Miyato
This figure shows the co-authorship network connecting the top 25 collaborators of Takeru Miyato.
A scholar is included among the top collaborators of Takeru Miyato 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 Takeru Miyato. Takeru Miyato is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
8 of 8 papers shown
1.
Najafi, Amir, Shin‐ichi Maeda, Masanori Koyama, & Takeru Miyato. (2019). Robustness to Adversarial Perturbations in Learning from Incomplete Data. Neural Information Processing Systems. 32. 5541–5551.5 indexed citations
2.
Suzuki, Ryôhei, et al.. (2018). Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration.. arXiv (Cornell University).3 indexed citations
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.