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Publications in Math-Net.Ru |
Citations |
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2024 |
| 1. |
A. D. Sosnovikov, D. Yu. Turdakov, “Automating the process of responding to a tax request using weak supervision”, Proceedings of ISP RAS, 36:3 (2024), 203–212  |
| 2. |
Ph. A. Kolokolnikov, V. V. Orlov, D. Yu. Turdakov, “Prospects for using a trusted information analytical system based on the Talisman platform using artificial intelligence methods to improve the efficiency of complex hardware systems”, Proceedings of ISP RAS, 36:3 (2024), 105–122 |
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2023 |
| 3. |
D. Shaikhelislamov, K. Lukyanov, N. Severin, M. Drobyshevskiy, I. Makarov, D. Turdakov, “A study of graph neural networks for link prediction on vulnerability to membership attacks”, Zap. Nauchn. Sem. POMI, 530 (2023), 113–127 |
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2022 |
| 4. |
D. Yu. Turdakov, A. I. Avetisyan, K. V. Arkhipenko, A. V. Antsiferova, D. S. Vatolin, S. S. Volkov, A. V. Gasnikov, D. A. Devyatkin, M. D. Drobyshevskiy, A. P. Kovalenko, M. I. Krivonosov, N. V. Lukashevich, V. A. Malykh, S. I. Nikolenko, I. V. Oseledets, A. I. Perminov, I. V. Sochenkov, M. M. Tihomirov, A. N. Fedotov, M. Yu. Khachay, “Trusted artificial intelligence: challenges and promising solutions”, Dokl. RAN. Math. Inf. Proc. Upr., 508 (2022), 13–18  ; Dokl. Math., 106:suppl. 1 (2022), S9–S13 |
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| 5. |
R. K. Pastukhov, M. D. Drobyshevskiy, D. Yu. Turdakov, “Detecting influential users in social networks based on bipartite comments graph”, Proceedings of ISP RAS, 34:5 (2022), 127–142 |
| 6. |
O. A. Mashkova, V. V. Shaklein, Yu. V. Markin, E. A. Karpulevitch, V. V. Ananev, A. A. Asatryan, Sh. T. Tigranyan, S. N. Skorik, D. Yu. Turdakov, “Methods for determining the elements of the PQRST-complex of the electrocardiogram”, Proceedings of ISP RAS, 34:4 (2022), 229–240 |
| 7. |
A. I. Perminov, D. Yu. Turdakov, O. V. Belyaeva, “Loss functions for train document image segmentation models”, Proceedings of ISP RAS, 34:2 (2022), 89–110 |
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2021 |
| 8. |
V. V. Ananev, S. N. Skorik, V. V. Shaklein, A. A. Avetisyan, Y. E. Teregulov, D. Yu. Turdakov, V. Gliner, A. Schuster, E. A. Karpulevich, “Assessment of the impact of non-architectural changes in the predictive model on the quality of ECG classification”, Proceedings of ISP RAS, 33:4 (2021), 87–98 |
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2020 |
| 9. |
K. A. Skorniakov, A. S. Laskina, D. Yu. Turdakov, “Two step method for grouping news with similar topics”, Proceedings of ISP RAS, 32:4 (2020), 165–174 |
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| 10. |
P. K. Andreev, V. V. Ananev, V. A. Makarov, E. A. Karpulevich, D. Y. Turdakov, “Diagnosis of left atrial and left ventricular hypertrophies using a deep neural network”, Proceedings of ISP RAS, 32:4 (2020), 141–154 |
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2019 |
| 11. |
M. A. Ryndin, D. Y. Turdakov, “Domain adaptation by proactive labeling”, Proceedings of ISP RAS, 31:5 (2019), 145–152 |
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| 12. |
A. A. Avetisyan, M. D. Drobyshevskiy, D. Yu. Turdakov, “Methods for news items popularity estimation on early stages”, Proceedings of ISP RAS, 31:5 (2019), 137–144 |
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2018 |
| 13. |
A. A. Avetisyan, M. D. Drobyshevskiy, D. Yu. Turdakov, “Methods for information spread analysis”, Proceedings of ISP RAS, 30:6 (2018), 199–220 |
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| 14. |
R. A. Gilyazev, D. U. Turdakov, “Active learning and crowdsourcing: a survey of annotation optimization methods”, Proceedings of ISP RAS, 30:2 (2018), 215–250 |
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2016 |
| 15. |
Y. S. Trofimovich, I. S. Kozlov, D. Y. Turdakov, “Approaches to estimate location of social network users based on social graph”, Proceedings of ISP RAS, 28:6 (2016), 185–196 |
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| 16. |
Mikhail Drobyshevskiy, Anton Korshunov, Denis Turdakov, “Parallel modularity computation for directed weighted graphs with overlapping communities”, Proceedings of ISP RAS, 28:6 (2016), 153–170 |
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2015 |
| 17. |
A. Aleksiyants, O. Borisenko, D. Turdakov, A. Sher, S. Kuznetsov, “Implementing Apache Spark jobs execution and Apache Spark cluster creation for OpenStack Sahara”, Proceedings of ISP RAS, 27:5 (2015), 35–48 |
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| 18. |
I. Andrianov, V. Mayorov, D. Turdakov, “Modern approaches to aspect-based sentiment analysis”, Proceedings of ISP RAS, 27:5 (2015), 5–22 |
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