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.
Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning
2017364 citationsSangdoo Yun, Jongwon Choi et al.profile →
A Comprehensive Overhaul of Feature Distillation
2019357 citationsSangdoo Yun, Jin Young Choi et al.profile →
Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons
2019276 citationsSangdoo Yun, Jin Young Choi et al.Proceedings of the AAAI Conference on Artificial Intelligenceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Jin Young Choi
Since
Specialization
Citations
This map shows the geographic impact of Jin Young Choi'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 Jin Young Choi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jin Young Choi more than expected).
This network shows the impact of papers produced by Jin Young Choi. 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 Jin Young Choi. The network helps show where Jin Young Choi may publish in the future.
Co-authorship network of co-authors of Jin Young Choi
This figure shows the co-authorship network connecting the top 25 collaborators of Jin Young Choi.
A scholar is included among the top collaborators of Jin Young Choi 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 Jin Young Choi. Jin Young Choi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Chang, Hyung Jin, et al.. (2019). PMnet: learning of disentangled pose and movement for unsupervised motion retargeting. University of Birmingham Research Portal (University of Birmingham). 136.10 indexed citations
Kang, Hee Jin, et al.. (2017). Time Domain Decision-Making Support Based on Ship Behavior Monitoring and Flooding Simulation Database for On-Board Damage Control. The 27th International Ocean and Polar Engineering Conference.4 indexed citations
Choi, Jin Young, et al.. (2007). Perfect Tracking Control for Linear Systems with State Constraint. International Journal of Control Automation and Systems. 5(2). 218–222.5 indexed citations
14.
Chwa, Dongkyoung, et al.. (2006). Multi-Input Multi-Output Nonlinear Autopilot Design for Ship-to-Ship Missiles. International Journal of Control Automation and Systems. 4(2). 255–270.14 indexed citations
Choi, Jin Young, et al.. (2006). Adaptive Feedback Linearization Control Based on Airgap Flux Model for Induction Motors. International Journal of Control Automation and Systems. 4(4). 414–427.7 indexed citations
17.
Anavatti, Sreenatha G., et al.. (2004). Design and Implementation of Fuzzy Logic Controller for Wing Rock. International Journal of Control Automation and Systems. 2(4). 494–500.11 indexed citations
18.
Choi, Jin Young, et al.. (2003). Robot arm trajectory planning using missile guidance algorithm. Society of Instrument and Control Engineers of Japan. 2. 2056–2061.
19.
Choi, Jin Young, et al.. (2002). Robust Predictive Control of Uncertain Nonlinear System With Constrained Input. 4(4). 289–295.1 indexed citations
20.
Choi, Jin Young & Chong‐Ho Choi. (1995). Partially trained neural networks with self-localizing capability for function approximations. Neural, Parallel & Scientific Computations archive. 3(1). 35–49.1 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.