А.В. Куракин

3.8k total citations
8 papers, 361 citations indexed

About

А.В. Куракин is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Political Science and International Relations. According to data from OpenAlex, А.В. Куракин has authored 8 papers receiving a total of 361 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 1 paper in Political Science and International Relations. Recurrent topics in А.В. Куракин's work include Multimodal Machine Learning Applications (2 papers), Adversarial Robustness in Machine Learning (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). А.В. Куракин is often cited by papers focused on Multimodal Machine Learning Applications (2 papers), Adversarial Robustness in Machine Learning (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). А.В. Куракин collaborates with scholars based in United States and Japan. А.В. Куракин's co-authors include David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Kihyuk Sohn, Colin Raffel, Han Zhang, Nicolas Papernot, Shreya Shankar, Brian Cheung and Gamaleldin F. Elsayed and has published in prestigious journals such as Journal of Artificial Intelligence Research, arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

А.В. Куракин

6 papers receiving 344 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
А.В. Куракин United States	6	284	151	23	21	16	8	361
Md Zakir Hossain Australia	3	229 0.8×	410 2.7×	19 0.8×	22 1.0×	12 0.8×	4	538
Jingcai Guo Hong Kong	11	178 0.6×	118 0.8×	29 1.3×	22 1.0×	8 0.5×	38	270
Litao Yu Australia	8	174 0.6×	262 1.7×	23 1.0×	18 0.9×	8 0.5×	27	399
Juhua Hu United States	9	246 0.9×	249 1.6×	18 0.8×	27 1.3×	8 0.5×	27	390
Nanyi Fei China	5	155 0.5×	141 0.9×	32 1.4×	13 0.6×	8 0.5×	9	302
S. Chitrakala India	10	306 1.1×	162 1.1×	12 0.5×	11 0.5×	14 0.9×	52	497
Muhammad Uzair Khattak United Arab Emirates	5	287 1.0×	298 2.0×	30 1.3×	19 0.9×	6 0.4×	10	468
Mantas Mazeika United States	5	384 1.4×	136 0.9×	18 0.8×	22 1.0×	4 0.3×	5	442
Saksham Singhal China	7	368 1.3×	289 1.9×	15 0.7×	25 1.2×	6 0.4×	10	507
Md. Abul Ala Walid Bangladesh	9	113 0.4×	64 0.4×	46 2.0×	10 0.5×	11 0.7×	45	255

Countries citing papers authored by А.В. Куракин

Since Specialization

Citations

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

Fields of papers citing papers by А.В. Куракин

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by А.В. Куракин. 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 А.В. Куракин. The network helps show where А.В. Куракин may publish in the future.

Co-authorship network of co-authors of А.В. Куракин

This figure shows the co-authorship network connecting the top 25 collaborators of А.В. Куракин. A scholar is included among the top collaborators of А.В. Куракин 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 А.В. Куракин. А.В. Куракин is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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8 of 8 papers shown

Ponomareva, Natalia, А.В. Куракин, Zheng Xu, et al.. (2023). How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. Journal of Artificial Intelligence Research. 77. 1113–1201. 78 indexed citations

Berthelot, David, Nicholas Carlini, Ekin D. Cubuk, et al.. (2020). ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring. arXiv (Cornell University). 165 indexed citations

Sohn, Kihyuk, David Berthelot, Chunliang Li, et al.. (2020). FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. Neural Information Processing Systems. 33. 596–608. 28 indexed citations

Куракин, А.В., et al.. (2020). Правовой механизм реализации принципов разграничения полномочий по предметам совместного ведения Российской Федерации и субъектов Российской Федерации в актах Конституционного Суда РФ. 14–19.

Куракин, А.В., et al.. (2020). Проблемы определения субъектов административной юрисдикции по делам об административных правонарушениях, структурированных в КоАП РФ и Кодексе об административных правонарушениях города Москвы в период пандемии коронавируса. 80–87.

Jagielski, Matthew, Nicholas Carlini, David Berthelot, А.В. Куракин, & Nicolas Papernot. (2019). High-Fidelity Extraction of Neural Network Models.. arXiv (Cornell University). 12 indexed citations

Elsayed, Gamaleldin F., Shreya Shankar, Brian Cheung, et al.. (2018). Adversarial Examples that Fool both Human and Computer Vision. 11 indexed citations

Elsayed, Gamaleldin F., Shreya Shankar, Brian Cheung, et al.. (2018). Adversarial Examples that Fool both Computer Vision and Time-Limited Humans. arXiv (Cornell University). 31. 3910–3920. 67 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.

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