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.
Diffusion Models in Vision: A Survey
2023824 citationsVlad Hondru, Radu Tudor Ionescu et al.IEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video
2019283 citationsRadu Tudor Ionescu, Fahad Shahbaz Khan et al.profile →
Curriculum Learning: A Survey
2022189 citationsPetru Soviany, Radu Tudor Ionescu et al.International Journal of Computer Visionprofile →
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
2022177 citationsNicolae-Cătălin Ristea, Radu Tudor Ionescu et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Radu Tudor Ionescu
Since
Specialization
Citations
This map shows the geographic impact of Radu Tudor Ionescu'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 Radu Tudor Ionescu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Radu Tudor Ionescu more than expected).
Fields of papers citing papers by Radu Tudor Ionescu
This network shows the impact of papers produced by Radu Tudor Ionescu. 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 Radu Tudor Ionescu. The network helps show where Radu Tudor Ionescu may publish in the future.
Co-authorship network of co-authors of Radu Tudor Ionescu
This figure shows the co-authorship network connecting the top 25 collaborators of Radu Tudor Ionescu.
A scholar is included among the top collaborators of Radu Tudor Ionescu 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 Radu Tudor Ionescu. Radu Tudor Ionescu 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
1.
Hondru, Vlad, et al.. (2025). Masked Image Modeling: A Survey. International Journal of Computer Vision. 133(10). 7154–7200.2 indexed citations
Soviany, Petru, Radu Tudor Ionescu, Paolo Rota, & Nicu Sebe. (2022). Curriculum Learning: A Survey. International Journal of Computer Vision. 130(6). 1526–1565.189 indexed citations breakdown →
Stan, Adriana, Traian Rebedea, Dani Yogatama, et al.. (2021). LiRo: Benchmark and leaderboard for Romanian language tasks. Neural Information Processing Systems.10 indexed citations
Ionescu, Radu Tudor, et al.. (2020). Black-Box Ripper: Copying black-box models using generative evolutionary algorithms. Neural Information Processing Systems. 33. 20120–20129.1 indexed citations
16.
Hovy, Dirk, Radu Tudor Ionescu, Tommi Jauhiainen, et al.. (2020). A Report on the VarDial Evaluation Campaign 2020. Työväentutkimus Vuosikirja. 1–14.22 indexed citations
17.
Ionescu, Radu Tudor, et al.. (2018). UnibucKernel Reloaded: First Place in Arabic Dialect Identification for the Second Year in a Row. arXiv (Cornell University). 77–87.10 indexed citations
Ionescu, Radu Tudor & Marius Popescu. (2016). UnibucKernel: An Approach for Arabic Dialect Identification Based on Multiple String Kernels. International Conference on Computational Linguistics. 135–144.20 indexed citations
20.
Popescu, Marius & Radu Tudor Ionescu. (2013). The Story of the Characters, the DNA and the Native Language. 270–278.19 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.