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.
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models
Countries citing papers authored by Youngjune Gwon
Since
Specialization
Citations
This map shows the geographic impact of Youngjune Gwon'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 Youngjune Gwon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Youngjune Gwon more than expected).
This network shows the impact of papers produced by Youngjune Gwon. 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 Youngjune Gwon. The network helps show where Youngjune Gwon may publish in the future.
Co-authorship network of co-authors of Youngjune Gwon
This figure shows the co-authorship network connecting the top 25 collaborators of Youngjune Gwon.
A scholar is included among the top collaborators of Youngjune Gwon 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 Youngjune Gwon. Youngjune Gwon is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gwon, Youngjune, et al.. (2020). POMO: Policy Optimization with Multiple Optima for Reinforcement Learning. Neural Information Processing Systems. 33. 21188–21198.1 indexed citations
5.
Brandstein, M.S., et al.. (2018). American Sign Language Recognition and Translation Feasibility Study. ePrints Soton (University of Southampton).1 indexed citations
Gwon, Youngjune, H. T. Kung, & Dario Vlah. (2011). DISTROY: detecting integrated circuit Trojans with compressive measurements. 3–3.14 indexed citations
14.
Gwon, Youngjune, H. T. Kung, & Dario Vlah. (2011). Compressive Sensing with Directly Recoverable Optimal Basis and Applications in Spectrum Sensing. Digital Access to Scholarship at Harvard (DASH) (Harvard University).2 indexed citations
15.
Gwon, Youngjune, James Kempf, & Alper Yeğin. (2008). Scalability and Robustness Analysis of IPv6 Mobility Protocols.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.